{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alber/tmp/venv/py3.5/lib/python3.5/site-packages/h5py-2.7.1-py3.5-linux-x86_64.egg/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import imp\n",
    "import keras.backend\n",
    "import keras.models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import pickle\n",
    "import time\n",
    "import keras\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Model\n",
    "from keras import optimizers\n",
    "\n",
    "from matplotlib import cm, transforms\n",
    "\n",
    "import innvestigate\n",
    "import innvestigate.applications\n",
    "import innvestigate.applications.mnist\n",
    "import innvestigate.utils as iutils\n",
    "import innvestigate.utils.visualizations as ivis\n",
    "from innvestigate.utils.tests.networks import base as network_base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we are going to build a sentiment analysis classifer, inspired by experiments in [Arras et al. (2017a)][arras] and [Arras et al. (2017b)][arras2]. In particular, we are going to predict sentiments of movie reviews, and apply explanation methods provided by iNNvestigate to analyze how words in each review influence the review's sentiment prediction.\n",
    "\n",
    "The dataset that we are going to use is [Standford Sentiment Treebank][standford], which has reviews in five categories: *very negative, negative, neutral, positive,* and *very positive*. In this example, we are interested in building a binary classifier, classifying negative and positive reviews (including their extreme classes). Neutral reviews are excluded. Nevertheless, we still provide a possibilty to build a five-class classifer in Section 2.2.\n",
    "\n",
    "\n",
    "This example is organized as follows: First, we obtain the dataset from the source as well as pretrained word embedding. Secondly, we prepare the dataset for training a neural network. Then, we construct a neural network model, receiving reviews as input and predicting their sentiments. Finally, we apply various explanation methods implemented in iNNvestigate to explain decisions from a trained model. The figure below is explanations of a review that we expect to see: red indicates a high relevance score in favour of the prediction, while blue is the opposite.\n",
    "\n",
    "![][sample]\n",
    "\n",
    "[arras]: http://www.aclweb.org/anthology/W16-1601\n",
    "[arras2]: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181142\n",
    "[standford]: https://nlp.stanford.edu/sentiment/\n",
    "[sample]: https://i.imgur.com/IRQL5oh.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "## Downloading The Dataset and Word Embedding\n",
    "\n",
    "In the first step, we download the dataset from `http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip`.\n",
    "\n",
    "For an *Unix* environment, one can achieve it by using the command below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  stanfordSentimentTreebank.zip\n",
      "   creating: stanfordSentimentTreebank/\n",
      "  inflating: stanfordSentimentTreebank/datasetSentences.txt  \n",
      "   creating: __MACOSX/\n",
      "   creating: __MACOSX/stanfordSentimentTreebank/\n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._datasetSentences.txt  \n",
      "  inflating: stanfordSentimentTreebank/datasetSplit.txt  \n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._datasetSplit.txt  \n",
      "  inflating: stanfordSentimentTreebank/dictionary.txt  \n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._dictionary.txt  \n",
      "  inflating: stanfordSentimentTreebank/original_rt_snippets.txt  \n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._original_rt_snippets.txt  \n",
      "  inflating: stanfordSentimentTreebank/README.txt  \n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._README.txt  \n",
      "  inflating: stanfordSentimentTreebank/sentiment_labels.txt  \n",
      "  inflating: __MACOSX/stanfordSentimentTreebank/._sentiment_labels.txt  \n",
      "  inflating: stanfordSentimentTreebank/SOStr.txt  \n",
      "  inflating: stanfordSentimentTreebank/STree.txt  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "  0   329    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "\r",
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      "100 6223k  100 6223k    0     0   182k      0  0:00:34  0:00:34 --:--:--  244k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "if [ ! -d \"./stanfordSentimentTreebank\" ]; then\n",
    "    curl -L http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip -O && unzip stanfordSentimentTreebank.zip\n",
    "else\n",
    "    echo \"The data is already there. Skip downloading!!\"\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, as we are going to use an embedding layer as the first layer of our model, embedding weights are required. Fortuanately, there are pretrained word embedding available for this dataset, so we do not need to train it from scratch. The pre-trained embedding is provided by L. Arras at [LRP_for_LSTM][leila_lstm_repo]. Therefore, we can simply download the embedding weights and vocabulary files.\n",
    "```\n",
    "- https://github.com/ArrasL/LRP_for_LSTM/raw/master/model/embeddings.npy\n",
    "- https://github.com/ArrasL/LRP_for_LSTM/raw/master/model/vocab\n",
    "```\n",
    "\n",
    "[leila_lstm_repo]: https://github.com/ArrasL/LRP_for_LSTM/tree/master/model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
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      "\r",
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      " 11 9158k   11 1088k    0     0   347k      0  0:00:26  0:00:03  0:00:23 1173k\r",
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      "100 9158k  100 9158k    0     0  1039k      0  0:00:08  0:00:08 --:--:-- 1404k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r",
      "100   138  100   138    0     0    232      0 --:--:-- --:--:-- --:--:--   232\n",
      "\r",
      "100  207k  100  207k    0     0   215k      0 --:--:-- --:--:-- --:--:--  215k\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "if [ ! -e \"./stanfordSentimentTreebank/embeddings.npy\" ]; then\n",
    "    curl -L https://github.com/ArrasL/LRP_for_LSTM/raw/master/model/embeddings.npy -o stanfordSentimentTreebank/embeddings.npy &&\n",
    "        curl -L https://github.com/ArrasL/LRP_for_LSTM/raw/master/model/vocab -o stanfordSentimentTreebank/vocab\n",
    "else\n",
    "    echo \"The data is already there. Skip downloading!!\"\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, due to an encoding issue in the dataset, we need to install the `ftfy` package for fixing the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: 1: pip: not found\r\n"
     ]
    }
   ],
   "source": [
    "!pip install ftfy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ftfy import fix_encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = './stanfordSentimentTreebank'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 19539 vocabs.\n"
     ]
    }
   ],
   "source": [
    "with open('%s/vocab' % DATA_PATH, 'rb') as f:\n",
    "    vocabs = pickle.load(f) \n",
    "    total_vocabs = len(vocabs) \n",
    "\n",
    "    # Unknown vocabs are set to <UNK>.\n",
    "    encoder = dict(zip(['<UNK>'] + vocabs, range(0, len(vocabs) +1)))\n",
    "    decoder = dict(zip(encoder.values(), encoder.keys()))\n",
    "    \n",
    "    print('We have %d vocabs.' % len(encoder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_embedding = np.load('%s/embeddings.npy' % DATA_PATH)\n",
    "\n",
    "# Unknown vocabs will have embedding weights of zero.\n",
    "embedding = np.zeros((pretrained_embedding.shape[0]+1, pretrained_embedding.shape[1]))\n",
    "embedding[1:, :] = pretrained_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence_index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>phase</th>\n",
       "      <th>sostr</th>\n",
       "      <th>splitset_label</th>\n",
       "      <th>phase_id</th>\n",
       "      <th>sentiment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The|Rock|is|destined|to|be|the|21st|Century|'s...</td>\n",
       "      <td>1</td>\n",
       "      <td>226166</td>\n",
       "      <td>0.69444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The|gorgeously|elaborate|continuation|of|``|Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>226300</td>\n",
       "      <td>0.83333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Effective but too-tepid biopic</td>\n",
       "      <td>Effective but too-tepid biopic</td>\n",
       "      <td>Effective|but|too-tepid|biopic</td>\n",
       "      <td>2</td>\n",
       "      <td>13995</td>\n",
       "      <td>0.51389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If|you|sometimes|like|to|go|to|the|movies|to|h...</td>\n",
       "      <td>2</td>\n",
       "      <td>14123</td>\n",
       "      <td>0.73611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges|as|something|rare|,|an|issue|movie|tha...</td>\n",
       "      <td>2</td>\n",
       "      <td>13999</td>\n",
       "      <td>0.86111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentence_index                                           sentence  \\\n",
       "0               1  The Rock is destined to be the 21st Century 's...   \n",
       "1               2  The gorgeously elaborate continuation of `` Th...   \n",
       "2               3                     Effective but too-tepid biopic   \n",
       "3               4  If you sometimes like to go to the movies to h...   \n",
       "4               5  Emerges as something rare , an issue movie tha...   \n",
       "\n",
       "                                               phase  \\\n",
       "0  The Rock is destined to be the 21st Century 's...   \n",
       "1  The gorgeously elaborate continuation of `` Th...   \n",
       "2                     Effective but too-tepid biopic   \n",
       "3  If you sometimes like to go to the movies to h...   \n",
       "4  Emerges as something rare , an issue movie tha...   \n",
       "\n",
       "                                               sostr  splitset_label  \\\n",
       "0  The|Rock|is|destined|to|be|the|21st|Century|'s...               1   \n",
       "1  The|gorgeously|elaborate|continuation|of|``|Th...               1   \n",
       "2                     Effective|but|too-tepid|biopic               2   \n",
       "3  If|you|sometimes|like|to|go|to|the|movies|to|h...               2   \n",
       "4  Emerges|as|something|rare|,|an|issue|movie|tha...               2   \n",
       "\n",
       "   phase_id  sentiment_value  \n",
       "0    226166          0.69444  \n",
       "1    226300          0.83333  \n",
       "2     13995          0.51389  \n",
       "3     14123          0.73611  \n",
       "4     13999          0.86111  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load all necessary files\n",
    "df_reviews = pd.read_csv('%s/datasetSentences.txt' % DATA_PATH, sep='\\t')\n",
    "\n",
    "df_reviews['phase'] = df_reviews.sentence.apply(lambda s: fix_encoding(s))\\\n",
    "    .apply(lambda s: s.replace('-LRB-', '(').replace('-RRB-', ')'))\n",
    "\n",
    "df_reviews['sostr'] = pd.read_csv('%s/SOStr.txt' % DATA_PATH,\n",
    "                                  sep='\\t',encoding='utf-8',\n",
    "                                  header=None, names=['sostr']\n",
    "                                 )\n",
    "\n",
    "df_reviews['splitset_label'] = pd.read_csv('%s/datasetSplit.txt' % DATA_PATH,\n",
    "                                           sep=',', header=0\n",
    "                                          )['splitset_label']\n",
    "\n",
    "\n",
    "df_phases = pd.read_csv('%s/dictionary.txt' % DATA_PATH,\n",
    "                        sep='|', names=['phase', 'phase_id']\n",
    "                       )\n",
    "\n",
    "df_sentiment_labels = pd.read_csv('%s/sentiment_labels.txt' % DATA_PATH,\n",
    "                                  sep='|', names=['phase_id', 'sentiment_value'],\n",
    "                                  header=0\n",
    "                                 )\n",
    "\n",
    "df_reviews_with_sentiment_value = df_reviews.merge(df_phases, how='inner', on=['phase'])\\\n",
    "    .merge(df_sentiment_labels, on='phase_id')\n",
    "\n",
    "df_reviews_with_sentiment_value[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to decretize `sentiment_values` to labels. According to `./stanfordSentimentTreebank/README.md`, we should use the  below discretization scheme.\n",
    "\n",
    "| Label | Sentiment Value Range |\n",
    "|-------|-----------------------|\n",
    "|   very_negative    |    [0, 0.2]                   |\n",
    "|    negative   |         (0.2, 0.4]              |\n",
    "|    neutral   |         (0.4, 0.6]               |\n",
    "|    positive   |        (0.6, 0.8]               |\n",
    "|    very_positive   |         (0.8, 1]               |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_discretizer(sentiment_value):\n",
    "    if 0 <= sentiment_value <= 0.2:\n",
    "        return 'very_negative'\n",
    "    elif 0.2 < sentiment_value <= 0.4:\n",
    "        return 'negative'\n",
    "    elif 0.4 < sentiment_value <= 0.6:\n",
    "        return 'neutral'\n",
    "    elif 0.6 < sentiment_value <= 0.8:\n",
    "        return 'positive'\n",
    "    elif 0.8 < sentiment_value <= 1:\n",
    "        return 'very_positive'\n",
    "    \n",
    "df_reviews_with_sentiment_value['label'] = df_reviews_with_sentiment_value.sentiment_value.apply(sentiment_discretizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence_index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>phase</th>\n",
       "      <th>sostr</th>\n",
       "      <th>splitset_label</th>\n",
       "      <th>phase_id</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The|Rock|is|destined|to|be|the|21st|Century|'s...</td>\n",
       "      <td>1</td>\n",
       "      <td>226166</td>\n",
       "      <td>0.69444</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The|gorgeously|elaborate|continuation|of|``|Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>226300</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>very_positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Effective but too-tepid biopic</td>\n",
       "      <td>Effective but too-tepid biopic</td>\n",
       "      <td>Effective|but|too-tepid|biopic</td>\n",
       "      <td>2</td>\n",
       "      <td>13995</td>\n",
       "      <td>0.51389</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If|you|sometimes|like|to|go|to|the|movies|to|h...</td>\n",
       "      <td>2</td>\n",
       "      <td>14123</td>\n",
       "      <td>0.73611</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges|as|something|rare|,|an|issue|movie|tha...</td>\n",
       "      <td>2</td>\n",
       "      <td>13999</td>\n",
       "      <td>0.86111</td>\n",
       "      <td>very_positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentence_index                                           sentence  \\\n",
       "0               1  The Rock is destined to be the 21st Century 's...   \n",
       "1               2  The gorgeously elaborate continuation of `` Th...   \n",
       "2               3                     Effective but too-tepid biopic   \n",
       "3               4  If you sometimes like to go to the movies to h...   \n",
       "4               5  Emerges as something rare , an issue movie tha...   \n",
       "\n",
       "                                               phase  \\\n",
       "0  The Rock is destined to be the 21st Century 's...   \n",
       "1  The gorgeously elaborate continuation of `` Th...   \n",
       "2                     Effective but too-tepid biopic   \n",
       "3  If you sometimes like to go to the movies to h...   \n",
       "4  Emerges as something rare , an issue movie tha...   \n",
       "\n",
       "                                               sostr  splitset_label  \\\n",
       "0  The|Rock|is|destined|to|be|the|21st|Century|'s...               1   \n",
       "1  The|gorgeously|elaborate|continuation|of|``|Th...               1   \n",
       "2                     Effective|but|too-tepid|biopic               2   \n",
       "3  If|you|sometimes|like|to|go|to|the|movies|to|h...               2   \n",
       "4  Emerges|as|something|rare|,|an|issue|movie|tha...               2   \n",
       "\n",
       "   phase_id  sentiment_value          label  \n",
       "0    226166          0.69444       positive  \n",
       "1    226300          0.83333  very_positive  \n",
       "2     13995          0.51389        neutral  \n",
       "3     14123          0.73611       positive  \n",
       "4     13999          0.86111  very_positive  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_with_sentiment_value[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11853"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_reviews_with_sentiment_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the goal of this notebook is to demonstrate how to apply **iNNvestigate** to text, we will simplify the problem (Five-class Classification) to a binary classification. Class 0 will contain very_negative and negative reviews, while Class 1 include very_positive and positive reviews. Neutral reviews are excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LABEL_MAPPING = {\n",
    "    'very_negative': 0,\n",
    "    'negative': 0,\n",
    "    'positive': 1, \n",
    "    'very_positive': 1\n",
    "}\n",
    "\n",
    "LABEL_IDX_TO_NAME = {\n",
    "    0: 'negative',\n",
    "    1: 'positive'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please use the following mappings for Five-class Classification\n",
    "# LABEL_MAPPING = dict(zip(['very_negative', 'negative', 'neutral', 'positive', 'very_positive'], range(5)))\n",
    "# LABEL_IDX_TO_NAME = dict(zip(LABEL_MAPPING.values(), LABEL_MAPPING.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 2 classes.\n"
     ]
    }
   ],
   "source": [
    "NUM_CLASSES = len(set(LABEL_MAPPING.values()))\n",
    "print('We have %d classes.' % NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence_index</th>\n",
       "      <th>sentence</th>\n",
       "      <th>phase</th>\n",
       "      <th>sostr</th>\n",
       "      <th>splitset_label</th>\n",
       "      <th>phase_id</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>label</th>\n",
       "      <th>label_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The Rock is destined to be the 21st Century 's...</td>\n",
       "      <td>The|Rock|is|destined|to|be|the|21st|Century|'s...</td>\n",
       "      <td>1</td>\n",
       "      <td>226166</td>\n",
       "      <td>0.69444</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The gorgeously elaborate continuation of `` Th...</td>\n",
       "      <td>The|gorgeously|elaborate|continuation|of|``|Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>226300</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>very_positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If you sometimes like to go to the movies to h...</td>\n",
       "      <td>If|you|sometimes|like|to|go|to|the|movies|to|h...</td>\n",
       "      <td>2</td>\n",
       "      <td>14123</td>\n",
       "      <td>0.73611</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges as something rare , an issue movie tha...</td>\n",
       "      <td>Emerges|as|something|rare|,|an|issue|movie|tha...</td>\n",
       "      <td>2</td>\n",
       "      <td>13999</td>\n",
       "      <td>0.86111</td>\n",
       "      <td>very_positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Offers that rare combination of entertainment ...</td>\n",
       "      <td>Offers that rare combination of entertainment ...</td>\n",
       "      <td>Offers|that|rare|combination|of|entertainment|...</td>\n",
       "      <td>2</td>\n",
       "      <td>14351</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>very_positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentence_index                                           sentence  \\\n",
       "0               1  The Rock is destined to be the 21st Century 's...   \n",
       "1               2  The gorgeously elaborate continuation of `` Th...   \n",
       "3               4  If you sometimes like to go to the movies to h...   \n",
       "4               5  Emerges as something rare , an issue movie tha...   \n",
       "6               7  Offers that rare combination of entertainment ...   \n",
       "\n",
       "                                               phase  \\\n",
       "0  The Rock is destined to be the 21st Century 's...   \n",
       "1  The gorgeously elaborate continuation of `` Th...   \n",
       "3  If you sometimes like to go to the movies to h...   \n",
       "4  Emerges as something rare , an issue movie tha...   \n",
       "6  Offers that rare combination of entertainment ...   \n",
       "\n",
       "                                               sostr  splitset_label  \\\n",
       "0  The|Rock|is|destined|to|be|the|21st|Century|'s...               1   \n",
       "1  The|gorgeously|elaborate|continuation|of|``|Th...               1   \n",
       "3  If|you|sometimes|like|to|go|to|the|movies|to|h...               2   \n",
       "4  Emerges|as|something|rare|,|an|issue|movie|tha...               2   \n",
       "6  Offers|that|rare|combination|of|entertainment|...               2   \n",
       "\n",
       "   phase_id  sentiment_value          label  label_idx  \n",
       "0    226166          0.69444       positive          1  \n",
       "1    226300          0.83333  very_positive          1  \n",
       "3     14123          0.73611       positive          1  \n",
       "4     13999          0.86111  very_positive          1  \n",
       "6     14351          0.83333  very_positive          1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_indices = df_reviews_with_sentiment_value.label.apply(lambda l: l in LABEL_MAPPING)\n",
    "\n",
    "df_reviews_with_sentiment_value_filtered = df_reviews_with_sentiment_value.loc[filtered_indices].copy()\n",
    "df_reviews_with_sentiment_value_filtered.loc[:, 'label_idx'] = df_reviews_with_sentiment_value_filtered.label\\\n",
    "    .apply(lambda l: LABEL_MAPPING[l])\n",
    "\n",
    "df_reviews_with_sentiment_value_filtered[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spiting Training, Testing, and Validation Set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPLIT_LABEL_MAPPING = {\n",
    "    'training' : 1,\n",
    "    'testing': 2,\n",
    "    'validation': 3\n",
    "}\n",
    "\n",
    "MAX_SEQ_LENGTH = 40\n",
    "EMBEDDING_DIM = embedding.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(ds):\n",
    "    filtered_indices = df_reviews_with_sentiment_value_filtered.splitset_label == SPLIT_LABEL_MAPPING[ds]\n",
    "    \n",
    "    reviews_in_ds = df_reviews_with_sentiment_value_filtered[filtered_indices]\n",
    "    \n",
    "    xd = np.zeros((len(reviews_in_ds), MAX_SEQ_LENGTH, EMBEDDING_DIM))\n",
    "    y = reviews_in_ds.label_idx.values.astype(int)\n",
    "    \n",
    "    reviews = []\n",
    "    for i, sostr in enumerate(reviews_in_ds.sostr.values):\n",
    "        sostr = sostr.lower()\n",
    "        review = []\n",
    "        for j, v in enumerate(sostr.split('|')[:MAX_SEQ_LENGTH]):\n",
    "            if v in encoder:\n",
    "                e_idx = encoder[v]\n",
    "            else:\n",
    "                e_idx = 0\n",
    "            \n",
    "            xd[i, j, :] = embedding[e_idx]\n",
    "            review.append(e_idx)\n",
    "        reviews.append(review)\n",
    "        \n",
    "\n",
    "    return dict(\n",
    "        x4d=np.expand_dims(xd, axis=1),\n",
    "        y=y,\n",
    "        encoded_reviews=reviews\n",
    "    )\n",
    "    \n",
    "\n",
    "DATASETS = dict()\n",
    "\n",
    "for ds in ['training', 'testing', 'validation']:\n",
    "    DATASETS[ds] = prepare_dataset(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11853, 9612)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_reviews_with_sentiment_value), len(df_reviews_with_sentiment_value_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 6920 reviews in the training set, and 1821 reviews in the testing set\n"
     ]
    }
   ],
   "source": [
    "print('We have %d reviews in the training set, and %d reviews in the testing set' % \n",
    "      (len(DATASETS['training']['x4d']), len(DATASETS['testing']['x4d']))\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(ID=1225): what it lacks in substance it makes up for in heart .\n"
     ]
    }
   ],
   "source": [
    "sample_idx = 1225\n",
    "\n",
    "print('Review(ID=%d): %s' %\n",
    "      (sample_idx, ' '.join(map(lambda x: decoder[x], DATASETS['training']['encoded_reviews'][sample_idx]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Construction\n",
    "\n",
    "Our classifier is a convolutional neural network, which was experimented in [Arras et al. (2017b)][arras2]. As shown below, the architecture has a convoluationa layer, convolving word embeddings of every two words, followed by a max pooling layer and a softmax layer.\n",
    "\n",
    "![][arch]\n",
    "\n",
    "[arch]: https://i.imgur.com/YQDfS5P.png\n",
    "[arras2]: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181142"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_network(input_shape, output_n, activation=None, dense_unit=256, dropout_rate=0.25):\n",
    "    if activation:\n",
    "        activation = \"relu\"\n",
    "\n",
    "    net = {}\n",
    "    net[\"in\"] = network_base.input_layer(shape=input_shape)\n",
    "    net[\"conv\"] = keras.layers.Conv2D(filters=100, kernel_size=(1,2), strides=(1, 1), padding='valid')(net[\"in\"])\n",
    "    net[\"pool\"] = keras.layers.MaxPooling2D(pool_size=(1, input_shape[2]-1), strides=(1,1))(net[\"conv\"])\n",
    "    net[\"out\"] = network_base.dense_layer(keras.layers.Flatten()(net[\"pool\"]), units=output_n, activation=activation)\n",
    "    net[\"sm_out\"] = network_base.softmax(net[\"out\"])\n",
    "\n",
    "\n",
    "    net.update({\n",
    "        \"input_shape\": input_shape,\n",
    "\n",
    "        \"output_n\": output_n,\n",
    "    })\n",
    "    return net\n",
    "\n",
    "net = build_network((None, 1, MAX_SEQ_LENGTH, EMBEDDING_DIM), NUM_CLASSES)\n",
    "model_without_softmax, model_with_softmax = Model(inputs=net['in'], outputs=net['out']), Model(inputs=net['in'], outputs=net['sm_out'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_one_hot(y):\n",
    "    return keras.utils.to_categorical(y, NUM_CLASSES)\n",
    "\n",
    "def train_model(model,  batch_size=128, epochs=20):\n",
    "    \n",
    "    x_train = DATASETS['training']['x4d']\n",
    "    y_train = to_one_hot(DATASETS['training']['y'])\n",
    "    \n",
    "    x_test = DATASETS['testing']['x4d']\n",
    "    y_test = to_one_hot(DATASETS['testing']['y'])\n",
    "    \n",
    "    x_val = DATASETS['validation']['x4d']\n",
    "    y_val = to_one_hot(DATASETS['validation']['y'])\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer=optimizers.Adam(),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    history = model.fit(x_train, y_train,\n",
    "                        batch_size=batch_size,\n",
    "                        epochs=epochs,\n",
    "                        verbose=1,\n",
    "                        validation_data=(x_val, y_val),\n",
    "                        shuffle=True\n",
    "                       )\n",
    "    score = model.evaluate(x_test, y_test, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6920 samples, validate on 871 samples\n",
      "Epoch 1/10\n",
      "6920/6920 [==============================] - 1s 145us/step - loss: 0.6041 - acc: 0.7272 - val_loss: 0.5263 - val_acc: 0.7968\n",
      "Epoch 2/10\n",
      "6920/6920 [==============================] - 1s 110us/step - loss: 0.4269 - acc: 0.8597 - val_loss: 0.4360 - val_acc: 0.8106\n",
      "Epoch 3/10\n",
      "6920/6920 [==============================] - 1s 104us/step - loss: 0.3399 - acc: 0.8747 - val_loss: 0.4111 - val_acc: 0.8083\n",
      "Epoch 4/10\n",
      "6920/6920 [==============================] - 1s 112us/step - loss: 0.3035 - acc: 0.8870 - val_loss: 0.4041 - val_acc: 0.8083\n",
      "Epoch 5/10\n",
      "6920/6920 [==============================] - 1s 111us/step - loss: 0.2838 - acc: 0.8935 - val_loss: 0.4056 - val_acc: 0.8209\n",
      "Epoch 6/10\n",
      "6920/6920 [==============================] - 1s 97us/step - loss: 0.2746 - acc: 0.8975 - val_loss: 0.4039 - val_acc: 0.8140\n",
      "Epoch 7/10\n",
      "6920/6920 [==============================] - 1s 104us/step - loss: 0.2628 - acc: 0.9019 - val_loss: 0.4096 - val_acc: 0.8197\n",
      "Epoch 8/10\n",
      "6920/6920 [==============================] - 1s 102us/step - loss: 0.2544 - acc: 0.9053 - val_loss: 0.4099 - val_acc: 0.8255\n",
      "Epoch 9/10\n",
      "6920/6920 [==============================] - 1s 98us/step - loss: 0.2484 - acc: 0.9087 - val_loss: 0.4116 - val_acc: 0.8232\n",
      "Epoch 10/10\n",
      "6920/6920 [==============================] - 1s 96us/step - loss: 0.2425 - acc: 0.9105 - val_loss: 0.4133 - val_acc: 0.8186\n",
      "Test loss: 0.3917355438564453\n",
      "Test accuracy: 0.8281164199752117\n"
     ]
    }
   ],
   "source": [
    "train_model(model_with_softmax, batch_size=256, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_without_softmax.set_weights(model_with_softmax.get_weights())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Analysis and Visualization\n",
    "\n",
    "At this stage, we have a trained model and are ready to explain it via **iNNvestigate**'s analyzers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify methods that you would like to use to explain the model. \n",
    "# Please refer to iNNvestigate's documents for available methods.\n",
    "methods = ['gradient', 'lrp.z', 'lrp.alpha_2_beta_1', 'pattern.attribution']\n",
    "kwargs = [{}, {}, {}, {'pattern_type': 'relu'}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alber/repos/innvestigate/innvestigate/analyzer/base.py:136: RuntimeWarning: This analyzer does not need to be trained. Still fit() is called.\n",
      "  \" Still fit() is called.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "28/28 [==============================] - 2s 76ms/step - loss: 2.0000 - broadcast_3_loss: 1.0000 - broadcast_4_loss: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# build an analyzer for each method\n",
    "analyzers = []\n",
    "\n",
    "for method, kws in zip(methods, kwargs):\n",
    "    analyzer = innvestigate.create_analyzer(method, model_without_softmax, **kws)\n",
    "    analyzer.fit(DATASETS['training']['x4d'], batch_size=256, verbose=1)\n",
    "    analyzers.append(analyzer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review 97 (2.3509s)\n",
      "Review 175 (0.0063s)\n",
      "Review 1793 (0.0058s)\n",
      "Review 1186 (0.0068s)\n",
      "Review 354 (0.0061s)\n",
      "Review 1043 (0.0060s)\n"
     ]
    }
   ],
   "source": [
    "# specify indices of reviews that we want to investigate\n",
    "test_sample_indices = [97, 175, 1793, 1186, 354, 1043]\n",
    "\n",
    "test_sample_preds = [None]*len(test_sample_indices)\n",
    "\n",
    "# a variable to store analysis results.\n",
    "analysis = np.zeros([len(test_sample_indices), len(analyzers), 1, MAX_SEQ_LENGTH])\n",
    "\n",
    "for i, ridx in enumerate(test_sample_indices):\n",
    "\n",
    "    x, y = DATASETS['testing']['x4d'][ridx], DATASETS['testing']['y'][ridx]\n",
    "\n",
    "    t_start = time.time()\n",
    "    x = x.reshape((1, 1, MAX_SEQ_LENGTH, EMBEDDING_DIM))    \n",
    "\n",
    "    presm = model_without_softmax.predict_on_batch(x)[0] #forward pass without softmax\n",
    "    prob = model_with_softmax.predict_on_batch(x)[0] #forward pass with softmax\n",
    "    y_hat = prob.argmax()\n",
    "    test_sample_preds[i] = y_hat\n",
    "    \n",
    "    for aidx, analyzer in enumerate(analyzers):\n",
    "\n",
    "        a = np.squeeze(analyzer.analyze(x))\n",
    "        a = np.sum(a, axis=1)\n",
    "\n",
    "        analysis[i, aidx] = a\n",
    "    t_elapsed = time.time() - t_start\n",
    "    print('Review %d (%.4fs)'% (ridx, t_elapsed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "To this point, we have all analysis results from iNNvestigate's analyzers, and we are now ready to visualize them in a insightful way. We will use relevance scores from explanation methods to highlight the words in each review. \n",
    "\n",
    "We will use  the *blue-white-red (bwr)* color map for this purpose. Hence, words that have a positive score to the prediction are be shaded in *red*, while  negative-contribution or zero-contribution words are then highlighted in *blue*, and *white*, respectively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a utility method visualizing the relevance scores of each word to the network's prediction. \n",
    "# one might skip understanding the function, and see its output first.\n",
    "def plot_text_heatmap(words, scores, title=\"\", width=10, height=0.2, verbose=0, max_word_per_line=20):\n",
    "    fig = plt.figure(figsize=(width, height))\n",
    "    \n",
    "    ax = plt.gca()\n",
    "\n",
    "    ax.set_title(title, loc='left')\n",
    "    tokens = words\n",
    "    if verbose > 0:\n",
    "        print('len words : %d | len scores : %d' % (len(words), len(scores)))\n",
    "\n",
    "    cmap = plt.cm.ScalarMappable(cmap=cm.bwr)\n",
    "    cmap.set_clim(0, 1)\n",
    "    \n",
    "    canvas = ax.figure.canvas\n",
    "    t = ax.transData\n",
    "\n",
    "    # normalize scores to the followings:\n",
    "    # - negative scores in [0, 0.5]\n",
    "    # - positive scores in (0.5, 1]\n",
    "    normalized_scores = 0.5 * scores / np.max(np.abs(scores)) + 0.5\n",
    "    \n",
    "    if verbose > 1:\n",
    "        print('Raw score')\n",
    "        print(scores)\n",
    "        print('Normalized score')\n",
    "        print(normalized_scores)\n",
    "\n",
    "    # make sure the heatmap doesn't overlap with the title\n",
    "    loc_y = -0.2\n",
    "\n",
    "    for i, token in enumerate(tokens):\n",
    "        *rgb, _ = cmap.to_rgba(normalized_scores[i], bytes=True)\n",
    "        color = '#%02x%02x%02x' % tuple(rgb)\n",
    "        \n",
    "        text = ax.text(0.0, loc_y, token,\n",
    "                       bbox={\n",
    "                           'facecolor': color,\n",
    "                           'pad': 5.0,\n",
    "                           'linewidth': 1,\n",
    "                           'boxstyle': 'round,pad=0.5'\n",
    "                       }, transform=t)\n",
    "\n",
    "        text.draw(canvas.get_renderer())\n",
    "        ex = text.get_window_extent()\n",
    "        \n",
    "        # create a new line if the line exceeds the length\n",
    "        if (i+1) % max_word_per_line == 0:\n",
    "            loc_y = loc_y -  2.5\n",
    "            t = ax.transData\n",
    "        else:\n",
    "            t = transforms.offset_copy(text._transform, x=ex.width+15, units='dots')\n",
    "\n",
    "    if verbose == 0:\n",
    "        ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### An Example of  The Visualization\n",
    "Let assume we have this review `I really love this movie but not in the beginning`. It is predicted as `positive`, and the relevance scores are distributed as follows: \n",
    "\n",
    "```\n",
    "        I 0.20\n",
    "    really 0.20\n",
    "      love 0.50\n",
    "      this 0.10\n",
    "     movie 0.10\n",
    "       but 0.10\n",
    "       not -0.20\n",
    "        in 0.05\n",
    "       the 0.05\n",
    " beginning 0.08\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mzbmUk8NVi4U+nTsz7ZNPeKpbNz7buJE+nTsX6dditbJz/36eeOGFa212hyMoMd9ZvTqtC/Iz4LHHePezz4LSb1lFRdXhzju948fGJpKVdRyrNZt77/Xe8daq1ZN8/HHpl2oD9UBCArVqei8RJsTFkXn6NG1atAhe/w88QK1a3vn2CU2akJmZSZUqVcjIyKBzly6Ad8He6OjooI15Z40atC64I3NAUhKvfvwxGceO0fnpp73jeTxEV6tG9tWr5Obl0argf6Df44+zdts2v30+/uCD6LRadFotd0RFcf7SJQDqxMSQ0NA75z4xLo7Ms/7nrfUs+L9OvO8+vvjmGwB+SE9n9dy5ADzSti23RUQE4beXJCnYKn0RFV4wYVkAnRs2ZNmQIUW225xORixbxu4JE7izalWmrFmDreCbYEmGtGnDq+vX06BGDZ5q1SpUoRehB9oDG/Fe4iu8J1wAc4GHQzy+Tvvn00TUKhWugjNxhW6LiOCXZcvYmJbGvFWrWP7NN/xr8uTgjK35c36+SqW69rNKpcLlclHSyuatGjfmtz/+IOvKFf73u++YVPBBV8jj8VDFZGJfampQ4rye740OiqIQplbj8Xjn8duCVKyVRKP5c6K+oqixWrNDOp4/uutuFlAX/K1C1r9ajcvlQghBXFwcaUGceH0937+rOTycuHvuIW3ZsiLt2VevlrnPkt5bvu35JeSvcD+1Wl3sfSlJUuVW6S/nFWpZpw47jh/ntwsXAMiz2/n1/PlrBdPtJhMWm42VZZiT0qJOHf64coXUn38mpXnzoMbZFm+R5AaygO/5cyXCPsBCvA+9eqSg7WHgQ7yPeQH4FcgLQhxmo5Fcq9+ncfh1MTsbj8dDr44deWX4cPYcPRqEKMqm7f33s3TDBsB7p9vtkZFEmEwoikKPhx7i2bfeomHt2kRVqVLkdREmE3ViYlixeTPgvfngl19/DUpMp/7v/0jbvx+A1A0baJOQQO2aNUk/fBiAVVv+XIDcHB5+U7kuD4MhEqPxNo4d2w7Ajz8upl4971kpvd6MzXbDx+Tdcmazmdzc0uOsX78+WVlZpKWlAeB0Ojl48GDQYjh17hxp+/YBkLpuHS2bNCHr8uVrbU6nk4PHjlElIgJzeDi7Ci57f/b110GLoSxaN23K8oL3xKYdO7hyE0WdJEkVp9KfiSpUzWxm0ZNPkrJgAfaCb3SvdOvGvdWrM7RNG+KnTaNGRATNY2PL1F/vxET2nT7NbeFBWS7nmh5AGtAE7yKZs4DCFbC6AP8AugOF31GHAJlAU7xnpaoB/xuEOKKqVKF1kybE9+6NQa+netWqpe5/5sIFnpo6FU/B5N7XRo4MQhRlM2XYMAZPm0bjvn0x6vX8z9Q/F/ju07kzzQcOZNGUKX5fu3T6dIa//jqvLFiA0+Wib5cuNAnCGkH1Y2N5f8UKBk+bxn116jA8OZkH4uJ4evp0Xpo3j/aJidf2TWrbluT//m++/O475o4fH7R5Ub4GDfofUlOfweGwcvvtdzNwoHf+TKtWg0hNfQaNxsDzz6cFdV5UMEVFRdG6dWviGzXCYDBQ3c8yKFqtlpUrVjB6zBhycnJwuVyMHTOGuJtYj6409evU4f3UVAZPmsR9desyqn9/Hm7dmtGvvkqOxeIdb+BA4urVY8H06QydPBmVotCueXMiy7m6e3lMHjGClOeeY/FXX9EqIYEat9+OOcjHKkmSAhfQs/MMBkPe6aNHjVE+k31LkpubS9Rdd9kdDse1NU0URTHrwsIu2d57r0KfsN71/ff5Z8eOdCzhsRS5NhtR48bZHS5X0Vjhkg0q/GnwB4AH4dQVIa5ViYqiNKodHf3DyTVrKmzCxL09e+YcO3WqoxAivbCtamTkb9+8/37dxIa+ay5WDmu+/57B06Ztz7py5cHCNkVRkh5q1mzx1nnzIkt7bTD4y1l4eNXfxoz5pm5sbGJpLw3Y8uVjnVu3vjNBCDH72thG47vTxo8fNe6ZZwLuf82mTQx+9tntWZcuFc3tQw8t3rplS8hzazKbbXl5ebWEEJcK2wx6fd7pb781+p65vFmWvDxMBYXL6/Pncy4ri3defDGwgMvI7nCgVqkICwsjbd8+hk+dyr7Vq/3ua0pMtOXl5xfJgSRJFaNCz0QFeot0MGRbrTzw+us0qVWrxAIKKkes16ss0VS2vJRF4Rpjt3L8Wzh2SAevBLkN2eNO1n3/Pa99/DEut5vYmjVZ9OqroRqqmFNnz9L72WfxCIFWo2H+tGkl7hvKHEiSVLqAiiiVSuUsXPukLPLz8wlTq31nfdudbneYEKJCHj5cxWjk11IOSIXynU7CVKrisUKYoGIePlwkHu+Ydt94bA5Hhc5rs9ntKqDIH10Be77dN7TKI99uR4DvpCW79Sb+dwPhL2eg2J3OwNZ0Kgu73eL2HdvucOTdzPu2NPk2G0KI4rkN8RyxQg6HQ43P73ezx6WS9Hn0Ufo8+mjA/ZRHvdq12fvFF2Xa1+FyFcuBJEkVI6APYJ1We2JvKY+78PVLRgZ6vb7Is1eEEA6jVnspo4Tbf2+VX06fRq/RFI8VLmXcingAAYd9mk9fuXpVczknp0JiuGqxkJWdrQVOXd/u9ngO7jlypNKeotpz5IjTarPt92n+9fDJk9pg33Hmq6ScCeE+eOrUnpDn7Pfff3biXTXjGrfbfXTn7t2WYPS/JyPDac3PL57bw4dDntujR4+i1Wot+BTIOo3mxN7Dvm+Vf09HT55EGxZWLAeSJFWMgIqo7JychQuXLCnzm3fhkiW2nKtXF/m2e4RYtmTXrtAecW/Swp07bTn5+Yt82z2wbAlUaKwCmA+52fBpkXYhrAa9flvh3WmhtmrrVow6XZoQosgtVjkWy+IFX35pqYyX+pwuF5+uW+ew2e2fX98uhMhUKcrpLT//HNLxS8pZfn7O4h07FoQ0Z1lZxzl//pga+NZn01dbd+zQXMkObNkEp9PJpytWlJzb6+5gDIUlS5d6FEVZ7nvJMjs3d+HC1av/I4qKJWvW+M2BJEkVI6AiSgixdNOWLZdfmTnTVdq3TpfLxcy33nKtWbcu2+PxfOq73epwvPPetm2WhTt3isJ1eG4Vl9vNzI0bXWv278/2CFE8VnjnPbAsBFERkdqB/wL7EfiDok+OASA7N3fyuDlz8jbs3BmyuSlCCDbv2sWoWbOsV3Jz/T1sb+PJM2dODJsxw26rRJf1LFYrPZ57zpqXn/8jsMd3+9W8vPF9JkzITw/BWYuy5OzixZMnli4dZnc6g38l5uLFTObM6WBVFGW6EKLIZWkhxGVtWNj89r165V24eLFc/Vvy8ujx9NPWPKvVf25zc8f36ds3Pz093c+rAyOEYPGSJWL27NmWvLy8t/xsX7pp587Lr8ybV+px6a9MCMHir74SsxctsuTl5xfLgSRJFSOgu/MAFEWpGRERsU54PPUf6dLFc1etWjq1Wq0AuN1ucfrMGfv6TZtUwG9Xc3O7CiFOldBPnFmnW6MNC6v+WHw81SMitKqKmCRVwO3xiNNXrtjXHzzojdVmKz1WWKOF6o8B1UGrCvI0KQd4ToJ9E2i0sCsHugsh/J46UBSlXbjBsDzSZDI+3KqVOioyUhuMBzULIbick+PYmJbmvnL1qi3PZksRQvg97aUoSkSkybTa4XS27tSihbNurVo6rUZzS9YhszscnsMnT9q+27NHq9No1l3Ny+snhPBb3alUqt56rXZ+zB13KB2aN9dEmkyaQP7tbjZnBkPkapfL0bphw07OatXq6tRqbQA5E9jtea4TJ9Ls584d0qhU6ml2e57f5+AoiqKYTabZDodjeGLjxo6mjRrpjEbjDedI2u12z+Fjx2zf/fijVqfVrruam1t6bvX6+TExMUqHDh00kZGRAeXW43aL8+fPO9Zv2CBsNtsli8XSTQjhdz6Boig1I0ymdUKI+o+0beu5q0aNa8elvzKPxyPOX7rkWL99u7DZ7ZcsVmuJOZAkKfQCLqKudaQodYCOQBR/nuHyAJeBLUKIE2Xs5z68T0WpQsXO365ssbqBC8AmIcQNJ4wp3k+nZkALIJgL2uTifXLNT2W5ZKAoSg28a4jewa1bh8wJnAU2CCEu32hnRVHUQBu8y3sFYzGeW5mzfLxzoDaXVNz4jB2OdwmzOoDuBrvDrc2twPtkpO3AwTLm1t9x6a/spnMgSVLoBK2IkiRJkiRJ+k/y7/DNTJIkSZIkqcLJIkqSJEmSJKkcZBElSZIkSZJUDrKIkiRJkiRJKgdZREmSJEmSJJWDLKIkSZIkSZLKQRZRkiRJkiRJ5SCLKEmSJEmSpHKQRZQkSZIkSVI5yCJKkiRJkiSpHGQRJUmSJEmSVA6yiJIkSZIkSSoHWURJkiRJkiSVw/8Ddgrf+cm21JgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa9c2db630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_text_heatmap(\n",
    "    \"I really love this movie but not in the beginning\".split(' '),\n",
    "    np.array([0.02, 0.2, 0.5, 0.1, 0.1, 0.1, -0.2, 0.05, 0.00, 0.08])\n",
    ")\n",
    "\n",
    "# \"love\" is shaded with strong red because its relevance score is rather high\n",
    "# \"not\" is highlighted in light blue because of its negative score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=97): this may not have the dramatic gut-wrenching impact of other holocaust films , but it 's a compelling story , mainly because of the way it 's told by the people who were there .\n",
      "Pred class : positive ✓\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92bf3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AIhQW1APw/lw+//8uwACAthwOBnG5LDZg3VfPnFmuvrpB3bqY+s47AolIVPC+eXi8P3fOnCIDDOBN39jD3f0DwM6J3xwOp29kRESZdO4FAgE6d+6sBdDFYlf3qKhobnl81g4PD4dMlt2WEGKhA05fINLJeqkOoK4eQJu8W4VCl4iOHYe4OrfuomnbdoBAJHKLyruNEFJFq9XVaNPGuR2OFi0CAdBKhJCaebe7u3tERURECZ1aeTkQETFA4OpaUNc6na5GB1OniqGUVK9aFXVr1ChgZy5iccSQPn3K3c6KAyEEg8LDBQTobrGrc4cOHbQikf1DfwMDA0FpQZvz8PCIiop6e2xuyODBrq6urpEWm1t6ValirFOrVrnIVFaEd+2KbLm8gL/isNl9I0NCyvQlnSXVK1dGXS+vgnYmkUQMGTiw2HYmEAjQOSTEaj8hIsL5/YSePSNYhJDwvNsIIUQml3fq26tXqcsdEhVlvd16eRnr1KlT6nIBYMCAAQI3Nyv+Q6+v0cEU3JSYiJAQ63oAOvX9jwbyxWEAhyNwI8S6rw4IsHVamRHRtav1+yaTderbt2+xywkPD0e2TNaWEMK2K8jQajTuXl5e9hRRImrXqsUDUDHvNj6fX61mzZrOnXBgAxcXF3C5XCMAi4eg1h0oC71Up7DQB5vN9fLwKLt7YgtPTy9Qaqxosbmih4en1hFzdgqDxWLB07OiFha6MRqNlatVK3/dOJpq1bxgNBbUtae7u9N1/f9A9SpVCtgZl8v18qpSpZwkKjk1qlblCIVCy8ZfqVatWg6ZHMFisVCxonWbK0sfURReXl7gcrmWr+MqeVWt+p/P5e4ikYDL4RTwV1qdzt2rUqVykuoN1StXLmhnPJ6XVzHenubFWj+Bx+NXq17d+f2EqlW9oNVqLIMiEQDi4eFR6nK9qlUDl8Mp2G69vOxut15eNvyHm1up/YdX5crQ6HTW9fAvnudkL14sFoyUFtS1h8db4au9qlSBRqu1u/3m7RvbFWRQgGVtAp9UKsWqVasAAMnJyQjv08fq+WPGjMGtW7eKXR+Xy2XB4usLm81ml+fEXTabbUSBL0KU5ZjEXfEAXhSyn0ss6yaEsNls63X/808qJk3ydYBcRWOWwbJ9sdhsjtWHYlaWFJs2mdrM+fPJiIsLt3ZYSeqn1uovTluZP38WvvtusV31l4QtW+Lx8uWb+/zBB2Nw507x7cJ8TQWvlc12SMfp2q1bOJic7IiiHErcp59id1JSge0v0tIQNWGCw+rhcjhW7cxWW2rfv7/D6i4J8YmJeJGWZnUfh8MBm8WyFJjF5XJL5PGXL1+OJk0aY9iwGGt1lNrmgPx+Ayjcd5QWDoeDgl+eweLakDH16VP4dupU7PLjJk7E7l9+sUNC+9h76BBu3b1rc781f0UpZXFsdHCWb9+Oxv37w6NTJyzcuBEAMOuHH7B482bHCW2mpHZmsxwb/QRbfvHp01R06FB8v3jmTDIuXjxndR+bzYGV7JosNotl9VncPiwMAJD65Am27dpls06b7dbGV4E6JRj65gz/wWGzrevBNOf/rWSWWo3FGg0AIE6lwm6dDgAwRqXCLUOBfBalwtwC7dL1svXroVQVyI/gEDhsNqxklWOxS9EWcp41ThkYJ5VKsWr16iKPW7duHZo0aeIMEf4jxKPwIOO/Q3a2FJs3ryr6wHJCr9c7reytW/MHGd9/vw6NGr09dnHtzh0cPHnSIWU5U485eFWpgt0rVji9Hluc++mncqk3fvdum0GGo1i9ehWOHDmKLVu2Orzs4vqN4lIWbe1tY++hQ7h1z2aWzBKzKjERR1evRubJk5g2cqTDyv03c/as7SCjpJw7fhxA0UEGQ/mxTihEk7fgK0MOyzZsKHGQYXBQkFQanBJkTPvsMzx8+BD+LVrg408+gVwuR1R0NBo1boyYYcOQk+AkJDQUKSkpMBgMiBs5Er5+fvBr1gxLly51iBypqalo3LgRRo6Mg49PQwwbFoNjx46hQ4cgNGzYABcvXsTFixfRvn07tGzZAkFB7XHX/BaoU6dgXLt2Lbesjh074Pr16/ZKBKAxgLEAmgLoBkAF4BqAtgCaAegHIBPAbgApAGIA+JuPsx+j0YBVq8Zi0qSm+OqrbtBoVDh6dC0+/jgAH37YHIsWDYBGo4RCkYV33qkNo9GU+l6tVmDs2JrQ63X4+++HmD27B6ZObYUZMzri+fM7dsu1YME0PHnyEN27+2Pu3I+hUMjx7rtRCAlphAkTYnLbzB9/XEZUVCf06tUKMTHdkZb20u66AeCbb+bB378hunbtgPv3TW2gZ88QfPrpZAQHt8aqVd/h4MFfEBoaiKCgFujTpwv++cfUoZs/fxbeeScW3bp1RJMmtbFv30+YOfMTBAb6oV+/HtCZ34gsXDgbnToFoE0bX0yY8A4opdi7dzeuXk3B6NExaN/eHyqVCj17huDKFVO2wqNHk9ChQ0u0a9cc4eFhDrlWAJjz/ffw6d4dHQYPxpAPP8Ti9esRMmwYUv78EwDwOiMDdUJDodVq8cV332HnwYPw79sXO3/9NV85l/74A/0/MM0l3XfsGIR+ftBqtVBrNKjXuTMAIGTYMEyeNw+t+/fHd5s24VVGBgaMH4+A/v0R0L8/zl6+DACYtXw5Rn32GUKGDUO9zp2xPM/b0s0//4xmffqgeZ8+GD51au72U5cuof2gQajXuXPuV43U58/h29s0RzL+p5/Q/4MP0GP0aDTo2hWfLFqUe+76xEQ07NYNbQYMwNgZMzD+q68coluJ+cVJ8vnz6DRwICLGjEG9jh0xbeFCbN27F20iIuDXvTsePnkCAIibMgXjpk9H6z590DA0FAdyOh7PnqFjdDRa9u6Nlr1745xZTwDw9erV8OveHc179MC0hQux++BBpPz5J2ImT4Z/z55QqdV2X8eSJUvg5+cLPz9fLFu2DOPGjcOjR4/Qq1dPhzyjlyxZAl8/P/j6+WHZsmX5/cbHHwOATd9x+fJldAoJQavWrdG9Rw+8fGl6DoSEhmLy5MloHRCA7777zm4ZAVOax7FTpqBpcDC6DRoElUqFazduoG2vXmgWGop+I0ciUyotcN7x06fRoksX+IWEYNTkydCY35JeunoV7cPD0bxzZ7Tp0QMyuRypT5+iY0QEWnbtipZdu+LcpUsAgOSzZxE+bFhumeM/+wzxO0zJoabNnYsmHTuiWWgops6ahXOXLmH/kSP4ePZs+IeF4WFqql3XPW7ePDx6/hw9J0zA0i1bMH7hwgLHhIwdiw8XL0brmBg07t8fl27eRP8pU9AgIgIzv/8egClVbe+JE9F80CD4Rkdj5+HDdsnlDPR6Pd59Nwbt2jXGyJFRUCqVaNGiDtLTTcvLXL2agr59Q/D0aSo2bfoBP/ywFCEh/jh//rRd9UrMQy6nffklTp87B/927bB05Uq7r6eSefjby5cvEdypE/xbtICvnx9On7ZP3hwiJ09GqyFD0LR/f/y4e7dDytys1aKZXI7mcjmGq1RINRrRWaFAM7kcYQoFnpr7InEqFd5TqdBWoUA9mQzJej1GqVRoLJcjLk/nW5KdjQ/VajQ1n//KfP5DoxE9FAq0ksvRUaHAnSI64CEKBVLMx0iyszFDrUZzuRxtFQqk5SmzrUIBP7kcM9VqSLKzHaIThVKJ3iNHonmPHvDt1g1fLVuGF//8g9AhQxA6eDAAYPu+ffDr3h2+3brh0wUL3lx/kyaYMncumvfogXkrVyJy7NjcfUdPn0a/d95xiIxF4ZRxRgsXLMCNGzdw7epVJCcnIyIyEjdv3ICXlxeCOnTA2bNnkTfbyLVr1/DXX3/hhrmDI7XywC4tDx48wK5diVi/fgPatAnAtm3bcPr0Gezfvx8LFszHpk2bcerUaXA4HBw7dgwzZkzH7t17MGrUaMTHx2PZsmW4d+8e1Go1mjdv7gCJ7gPYDmAtgIEA9gBYBGAFgE4AvgDwFYBlAFYCWAygtQPqNfHy5X189NF2vP/+WixePBAXLuxB27b90bWrqQFu2zYTx46tR+/eE1C3rj9u3jwJP79QpKQcgL9/d3A4XKxe/Q7effcHeHk1wL17v+PHH9/H7Nm/2SXXZ58txN27N3D48DWcP5+M0aMjcPz4TVSp4oV+/YJw6dJZtGgRiC++mID16/ehQoVK2L9/JxYtmoFvv91gV91Xr17G7t07cO7cNej1enTs2BL+/qYJ01qtFqdOmTr8mZmZ+O23CyCEID5+HZYuXYQFC74FADx+/BAHD57AnTu3EBbWDlu27MHcuYswZEg/JCX9ij59IvHOO+MxbdoXAICxY4fj0KEDiIyMwpo1KzFv3mK0bJn/Pr969QoTJoxFUtIp1KlTFxkZGXZdZw6X/vgDew4fxvX9+6HT6dCyXz+08rU+XIDH42H2pElI+fNPrPzyywL7WzRpgmu3bwMATqekwLdBA1z680/oDQYE5rEXrU6HFPMb/qEffYQP4+LQoXVrPH3xAt1HjcJtc4Bw59EjnEhIgEwuh0+PHnhvyBDcS03F3NWrcW7HDlT09ERGnufDy1evcGb7dtx59Ah9x41DVI8eBWS8dvs2ru7bBz6PB5/u3TFh+HCwWSzMWbUKV37+GS5iMTqPGIHmTsiSd/32bdw+fhyebm6oFxyMMYMG4eK+ffhuwwasiI/HMrNOU58/x8V9+/DwyROEDhmCB0FBqFyxIo4mJEAgEOD+48cYMnEiUn75BYdOnMC+o0fx+969EAmFyJBK4enujpWbNmHxjBlo3ayZ3XJfvnwZ8fEbceHC76CUom3bQCQkbMHhw0n47bcTqFjRclhxycvfGB+P3y9cAKUUgW3bYktCQq7fAEzDpa5evVrAdwQGBmLCxInYt3cvKlWqhJ07d2LGjBnYsMH0HNBqtUgxd9Idwf1Hj7B99Wqs/fZbDBw7Fnt+/RWLvv8eK+bNQ6f27fHF11/jq2+/xbI5c3LPUavViJs0CccTE9HQ2xsjxo/H6k2b8H5cHAa9+y52rlmDgBYtkC2TQSgQmO71zp2me/3oEYaMG4eUI0dsypSekYGfDx3CnTNnQAiBNCsL7m5u6NutG8K7dkWUA4aZ/TBjBpLOncOJNWtwoJDOKY/LRcrWrfhu2zZEfPghLm/dCk83N3j37YsPY2KQfPkyvCpVwq/LlwMAsmQyu2VzNA8e3MV3361HYGAQJk4chQ0brH9Vr1WrDmJjx0EslmD8+KlWjykNC7/6CouXL8cBB3XYL128CADYtm0bunfrhhkzZsBgMECpVDqk/A1ffQVPNzeo1GoExMRgQJcuqODuXurybhoMmKvV4pxIhIosFjIoRaxKhVguF7E8HjZotZioVmOvOUlFJqU4LxJhv16PvkolzorFWMdiIUChwDWDAf5sNhQAWrPZWCoQYLZGg680GqwUCvGOSoUfBAI0YLPxu16P99Vq/FbMDFoKAG3ZbMwTCPCJWo21Oh1m8vmYpFZjEo+HIVwuftDaXFOyxCSdPAmvKlXwq3moYlZ2NjYmJuLE9u2o6OmJF2lp+PTrr3H5l1/g4eaGbsOHY+/hw4js3h0KpRKB/v74duZMUErROCwMr9LTUalCBWxMTMSogQMdJmdhlMlkhjZt2qBGjRoAAP/mzZGampovyKhXrx4ePXqECRMmoHfv3ujWrZvD6q5bty78/PwAAE2bNkVYWBgIIfDz80NqaiqysrIQFxeL+/fvgxCS+9Y5Ojoac+fOwTfffIMNGzYgNjbOURLB9GUCAFoBeAhAClOAAQCxAKIdVFdBqlSpi7p1TfXXq9cK//yTiqdPb2DbtplQKKRQq+Xw9zcloQkKGoSzZ3fCzy8UZ87sQI8e70OlkuPu3XNYvPiNjDqdxuFy+vu3QbVqpjbTpIk/nj9PhaurO+7evYGhQ7sCMH0CrFy5ZJMCrXHu3Gn06dMPOVl2evZ8k0VhwIA32eZevHiOuLhB+Pvvl9BqtahT5824127deoLL5aJpUz8YDAZ07Wrq7DZt6oenT1MBAKdPn8CyZYugVCqRmZmBRo2aolcv252BS5cuICgoOLceT09Pu68VAM5euYKIsDAI+HwI+Hz0CQ0tdVkcDgfetWrh9oMHuPjHH/ho1CicunQJBqN05qdqAAAgAElEQVQRHVu/CZoG5cmscuzcOdx68CD372y5HHKFKf1775AQ8Hk88D09UdnTE2np6fjt/HlE9+iBiubr98zjzCK7dAGLxUKT+vWRll5g/S0AQFi7dnBzMaVMbuLtjSd//YXXmZnoFBCQW1Z0z5649/hxqfVgi4DmzVGtcmUAgHft2ugWHAwA8GvUCCfOn889bmB4OFgsFhrUrYt6tWrhzsOHqFuzJsZ/+SWu3boFNouVK9+xs2cxMjoaIqEpaZOnHc7dFmfOnEFkZD+Izc63X7/+DnsLmlN+v8jI3PL79+tntXxrvsPd3R03btxAV7OfMBgMyJtacdCgAhki7aJurVrwNwfhrZo1w8PUVEizs9GpfXsAQOygQYjO85YQAO4+fIi6tWqhobd37jHfb9yIsI4dUa1KFQSYs/a4mtulQqnE+OnTce3GDbDZbNx7VGDdxHy4ubpCwOdj9IcfIrxrV4R37erQay4Jfc1zVvzq10dTb29UM79Fr1e9Op6lpcGvfn1MWbIEn373HcI7dkTHli3LTVZbVK9eE4GBQQCAqKhhWLt2eTlL5BgCAgIwavRo6HQ6REZGwt/fv+iTisHybdvw84kTAIBnaWm4//SpXUHGbwYDojkcVDSP4PckBOcNBvxkfsYN53LxieZNP6MPl2vqx7HZqMJiwc88nKkpm41UoxH+bDZYAAaZ5/MM43LRX6mEnFKcMxgQneeLR0l6LzwA4eYyW7HZOGoeknneYMBes6xDuVxMdcCXZADw8/HBlLlz8emCBQgPC0PHNvmSr+HS9esIadsWlSpUAADEREbi1MWLiOzeHWw2GwN69gRgyjA4vH9/bPn5Z4yMjsb5K1eweckSh8hYFGUSZPD5b1Jfs9nsAmNlPTw8cP3aNRw+fBg/rFmDXbt25b6VcmTdLBYr928WiwW9Xo8vvvgcISGh+Omnn5GamorQ0BAAgEgkQpcuXbFv3z4kJu5CSspla8WXRqI8/2fDFGCUHRxOXn2wodWqsGJFHD79dC/q1m2O336Lx82byQCAgIC+2Lp1OmSyDDx6dBl+fp2h0SggErljyZJrNmpwDDxewTZDKUXDhk2xb9/5Qs50LCLRmzccU6dOwPjxH6F37744fToZ8+fPKiAvi8UC1/wAzPlbr9dDrVbjww/fx6lTKahRoybmz58FjcYxDyJHwWGz3wyPK+RtTPdRo5D2+jVa+/pi3fz5CA4IwKFTp8DlcNClfXvEffopDEYjvvnkk9xzxMI3WUyNRiMuJCZCwLdMiQ/weW8SwFh7VhR2vK2FRQuUWYbjU/PWzSIk928WIfnksJxpRwjB0vXrUaViRVw/dAhGoxECH5+yEPmtwprvoJSiadOmOH/O+rh4cQnz+hcpg0X7kTpoKERelq5ZY7rXv/1mute1awMwBfE5NgmYVnLP2X7x0CEcP30auw8cwMoNG/Dbnj0Ol6s45LZpFgv8PBORWSwW9AYDGtaujSvbtuHgmTOYuWoVwtq0wRdlNFSjuFimtiWE5NP92/asLi7BwcE4dfIkfv31V8SNHImPPvwQI0aMsKvM5EuXcOz333F+0yaIhEKEjB6d2y7LipynAgv5e1QsmJYqtgYBYATgTgiulXI9Ci7etBV2IXU5iob16uHKr7/i4IkTmLl4McKCgop9roDPR96MVSOjo9Fn9GgI+HxE9+6NskqY5JQ5GS4uLpCV4JPo69evYTQaMWDAAMydMwdXzJ/Ly4KsrCxUr14dABAfH59v35gxYzBp0kQEBATAnvRzheMGwANAzlu8BLz5quECwPmfllUqGTw8qkGv1+HUqTcTOoVCCerXD8CGDZPQqlU42Gw2RCJXVKlSF+fOJQIwdeweP7Z3rgogkbhAoSj8Wr29fZCe/gqXL5uCDJ1Oh7t3b9pdd1BQMA4c2AuVSgWZTIakJOvZYbKzs+DlZWorW7duKlEdavObjQoVKkIul2Pv3jefxSUSF8jlBa89IKAtzp49hdRU0xtsRw2XCmrZEr+cOAG1RgO5QoED5sxRdapXx+WbJn3mzdrkIhZDpniz0OzhDRtwbf9+rJs/HwDQsXVrLNu0Ce1atEAlT0+kS6W4+/gxfBs2tFp/tw4dsCIhIffva0VkmOvcrh0Sk5KQnpkJAPmGS5WWAD8/nLx0CZlZWdDr9dhTzuPEEw8ehNFoxMMnT/Do6VP41KuHLJkM1SpXBovFQsJPP+VO3uvaoQM2JibmTv7L0YeLRAKZXO4QeTp27Ih9+/ZCqVRCoVBg796f0bFjx6JPLEH5e/ftyy3/5717ERQUVCy/4ePjg1evXuH8+TfPgZs37X8OFBc3Fxd4uLnh9IULAICExER0atcuv4ze3kh99gwPzF+fco7x8fbGy7Q0XDL7OJlcDr1eb7rXVaqY7nViYu69rl2jBm7duweNRgNpVhaOm7/2yBUKZGVno1eXLlg6ezaum23IkW3AUbx49QoigQDDevfGxyNG4Mod++fw5RDWowf++usvu8t5/vwpLl0ytac9e7YhMLADatasg+vXTS8Xf/nlTQBn63ltDyXtMxWXJ0+eoEqVKhg7dizGjB6NK1eu2F1mllwOD1dXiIRC3Hn8GBfMw9ztoTObjUS9HunmoC6DUrRns7HDPLJkq06HjiWcfG0EsNv8kmqbTocObDZcCUFdFguJ5nIppbjugJdObdls7DHXlSOzI3iRlmaynX798PG77+LKjRsmGzf74zb+/jj5++94nZEBg8GA7fv3o1NgoNWyvKpUgVeVKpi7ciVGRjtutExYly6F2qBTQpkKFSogKCgIvn5+EAqFqFJEPvm//voLI0eNyn1rsMDceSkLPv74E8TFxWLevLno1Svfgppo1aoVXF1dERfn7KwamwCMA6AEUA/ARvP2OPN2IYDz5n8dz5AhczBtWiBcXSuhQYNAqNVvHnZBQYOweHE05sxJzt02efJWrFnzHhIT58Jg0KFDh8GoW9e++SoeHhXQunUQwsJ8IRAIUalSwTbD4/GwZs1ufPHFRMhkWTAY9Bg9ejJ8fJraVbe/f0sMGDAI7do1R6VKldGypfVFcT77bBZGjIiGu7sHgoM748mT4g+vcXd3R1zcWAQG+qJy5ar56hg2LA6TJo2DUCjE8eNvvtJUqlQJy5f/iJiY/jAajahUqTL27z9a+gs1E9CsGfp27oxmffqgSoUK8GvYEG4SCaaOHo2Bkyfjx5070TskJPf40MBALFyzBv59++Kzd9/FoN757SSweXOkvX6NYPNiQs18fPD369cF3g7msHzmTHzw1Vdo1qcP9Ho9ggMC8MPs2TblbdqgAWaMG4dOw4aBzWKhRZMmiP/6a7t0UL1qVUx/9120iYqCp5sbGtWrlzukqjyo5eWFNhERyJbL8cO8eRAIBHh/+HAMeO89bN6zBz06dYLYPJyvR0gIrt26hdZ9+oDH5aJXaCjmf/IJ4qKiMG7mTAj5fJz/+WcIBYJSy9OyZUvExsYhMND0eX706DFoUcqFuWyVHxcbizZmhzhm9Gi0atUq12/07NEDvS3aWQ48Hg+7ExMxcdIkZJmDxMmTJqFpU/ueAyVh0/LlGPfJJ1CqVKhXuzY2LluWb79AIMDGZcsQPXYs9Ho9Avz9MW7ECPB4POxcswYTZsyASq2GUCDAscREvB8XhwGjR2NzYiJ6hIbm3uua1atjYN++8A0JQd1atdDCPPRXJpcjIjYWao0GlFIsmTULADA4MhJjp0zB8vXrsXvdOnjbuVCbI/jz/n18vGyZ6Ssvh4PV06c7pFyj0YgHDx86ZBhp/fo+WL/+e0ycOAo+Pk0wcuR7aNmyDSZNGo0FCz5HUFBI7rHdu/fBqFFRSErahwULVqBdO/uD72a+vmCz2Wjeti3ihg3Dh+PH210mYJrX9M3ixeByuZBIJNi8qWQvx6zRIygIP+zejcb9+sGndm20NbdJe2jKZmMGj4dOSiXYAFqw2VghEGCkSoVvtFpUIgQbhSXr/4gBXDQYMFejQWVCsNN8/lahEO+pVJir0UAHYDCXi+Z2Zo9axudjmEqFeRoNenA4cHPQWiB/3rmDjxcsAIsQcLlcrJ47F+evXEGP2Fh4Va6MEzt2YOEnnyB0yBBQStE7NBQRhUw3iImIwKv0dDSuX98h8hmNRjx48KBQGyS2hhgUB6FQqHj+7Jmognk8mLOZPHmy7rvlyz+jlH6bs00sFi//6qvZE6ZMmeLw+l68eIHQ0BDcvn0HLBvZfl1cJGqFQlGDUpo7IJwQoQJ4LgKcrZee2UDSKEpp7msWV9eKR8eNW9OlXbsBTq67cJ48+RMzZwY/lcsza+dsI4T41ahR58z584+dvlJycHDDrMeP74dRSnPHuXl4eD7Yv/+od4sW/61VsG/e/BPduwc/lUrz67pO9epnHp84YVXXcoUCErEYSpUKwTEx+HHOHLQsw07a20CODvR6Pfp98AFGDRiAflYe0D1Hj85OOn06n51V9PQ8umb+/C45Y17tIW7KFISHhSHKjhWBi2JFfDxmfPPNumy5PHfyACFkwrvvvrto9eofSh+N5MHHp2HW/fv5bc7T0/PB0SNHvFu9JSvP//LLLxg1evTpV69eBedsI4T0CQ0KSvhtzx638pStLJDUq6dWKJX5/JWQz1c8T0oS2TOm3hH0HD8+O+ncufx2VrHi0TUrV3YZ0K9fgeNv3LyJDZs2YUmejHEAMHnqVN13K1fm6yeIROLl06bNnvDBB47vJ+RFJpOhYcMKGq1Wm2tThBAXPo+Xrs7IKPVS178cPIhR7713+tXr1/nbbWhowm/Hj9vVbv/8808Ed+r0NDPTwn94eZ15fPBgqXy1TKFAhU6dNFqdLr8egHS1q2uZLPktyc6G3NXpXQ0AgJJSCGEaSrVDp8N2nQ77zC8K8vKnwYBgheJpJqX5dV2jxpnHZ844XdjxX3yBFk2bYrSNeWsyuRwVWrQo2H75/HS1SlXgvt24cQMbNmzAEivzOyQuLmqFQlHD3i8ZNO94UWdjMBopLBZzMRqNTpFh8+bNmDlzBr79donNAMNcP7GUyfR3WejFAMu6KQWltOzuiS3M96RABFtW7cVG/WXaXsuK0uj6nc8/x60HD6DWaBDbr9//XYABALNWrMCxc+eg1mjQrUMHRNqYPGuwpl9K/1VtyWg0Wn0wGAyOu4Z/g83Z0sPbJKMzseWvjHa8bHQUJbUz36ZNCwQYAGAwGMqsn2CJqWkVfIttz8tcoJB264ChPs7w1Uaj0eqX7PJvZc7hssGA8Wo1KExzPjbY+Opi1mi59ItahYdDLBLh2xkzbB5jNBqttF7b7dfX19dqgGEuiwCgdgUZHA5HmZmZKc7JyexsXr96pQOQb9adRqPJyMjIsLLqtn2MGDGiyAlSer0eGo2GiwITJzhKIFMMOFsvGRQW+jAa9ZlyeaaT6y0ahSIThLAsB5lmy+XZZTLbSCbL5sBCN4QQmVRa/rpxNFJpJlisgrrOVihs6npbGWWWeJtZPG1asY7LkEoL2JneaMzMzMpyiBzx335b9EF2kpGVRVUazWuLzdmvX7922NzF7GzrNpeZ+fbYXGZmJoxGo+WknmxHzPN529Hr9dBotQX8FYfDUWZmZ4srOW3eYfHIyMoqaGcGQ2ZJ28/r16+t9hOkUsf3EyyRSjPB5XIt88QqdHo9W6PR5EtkUBIypVLr7dYB8/QyM0vuP4osMzsbXA6noB4AtoZS8B00nKgwyuorBgB05HBwvRiTyTMpBYuQgrqWy53eL7p84ECRx2RmZVlvvzpdidpv3r6xXQbHZrPPHD12rEyCU6PRiCNHjxIA+VKKUErP7t+/v1xmup04cQKurq73KKUWM33YZ4CjTtZLNoAbQphW7MtFqcw6mpJyoNxn/l29mqTXalXHLDY/0+t1akdM1i6Mx4/vQ6VS6AHkmzShVCqPHDt22HGzst4Sjh1L0qtUBXWt0+nUN+/fLxeZ/itky+W4cf9+ATvLys4+euD48XK3s+Ky78gRmcFgOGux+eyJE7+xHbEa7P3796FQWLe5w4ffHps7cOCAIjMz03IBiiv3Hj4UWFtQ77/EibNn4SqRFPBXbBbrzNHffy/Xl8zZcjluPHhQ0M6yso4eOHSo2HZmNBpx5NixAv0EgJ49dMj5/YTk5KPgcLj50h9SSo3ubm43j5sTbJSGA4cOKTKl0oLt9v59gb1BfNLhw9b9h16vvpkn1XhJOHrhArgcTkE9ADePF5Ex8L9Mkl6vV1Fq3Vffu1cuMuXl6Jkz1u+bu/vN4+ZFYotD3r6xXUFGVlbWxqVLlyqckRXBkp07d0Kn0/1DKbVMRZP8+PEjJNthwKVBr9dj0aKvVTKZzEqu3ayNwFKFczNDrTAAwvOUUkvP+PP160c5z54VnrHHmUilaTh2bJ1Wq1VtzbudUmqklG5bvfprrb2fj21BKcWqVQu1LBYrkVKar/ek0ai3JSRs0P39t2NWCX8b+OefNMTHr9Oq1VZ0DWz7+scfnabr/wdWJCQYhHy+VTs7euYM59a/IIg7m5KCOw8fsgHkc26U0geU0mdbtiTY1UAopfj6a+s2p1art23YuFGXsyJ3eXLv3j38evAgG8BPebdTSmVCgSB52dq1/9nej16vx9crV6pkCkUBf5Ull29cumWLIm8GubJmxc6dBqFAYN3Ojh3j3DIv+FkUOxMTbfYTnj59hDNnkh0hrlUUCgVWrfpWkZ0t3Wi5T5qVtfbrJUtUulJkHrp3/z5+PXzYersVCpOXLVtW6nablpaGdevWaVUq67766/j4EvsPhUqFbxMSFFKZrKAegLVfa7Uq3f+hT0ozGrFOp9OqAOu++ocfytVXK5RKfLt2rUKanV3wvkmla79etKhY7Vev1+PrRYty+8b2fjr8NS0tLbFDx47KgwcPQuvAlQ5zePLkCeYvWEDHjBkjk8lkBWYzU0r1arW6f58+4cqVK1fi77//drgMeTEajTh9+jTCw3urL168mGIwGFZYOexXIC0R6KAEDgJwpF7uAJhqABakA9I4y72U0nSDQffO9OkdlCdObIJCUXZv5zQaJc6f34Np09oqNRrlUkppiuUxKpXy86SkvffHjx+qvX49xe6xqjlQSvHHH5cxefII7S+/7HqsUMg/tXLMH1qtZmFISKBy797dDlv9tDxQKpXYt28PQkPbKlUq67pWqlSf7z127P7Qjz7Spvz5p8N0/f/AnYcPMXXhQsOCH35Il8pkcZb7KaXpOr3+nQ5RUcpNu3dD6qChU47kn9evsSohAT1GjFBqtNooSmmBB1F2dnb0Bx98kD1nzhxjampqicqnlOLy5cuIjR2h3bVr12O53LrNaTSahYFt2yp37y4fm8vKykJCQgLatW+v1Ov14ymlBZyENDt77LerV7+a/Pnn+lt375a5jM7CaDTi9IUL6D1smPri1as2/VVaRkZih1GjlAfPnIHWgSk4i+LO48eYunSpYcH69YXbWWioclNCAqQ2vjY9efIE8xctomPGjZPJ5HKb/YShQ8OV69atRFqa4/oJOp0Ox48noU+fYOXLl3/th0UwYK7/x2t//HGuR79+qpOnT6M4Xw+zsrKQsH072nXubLvdSqVjv12y5NXkyZP1t4pIBZ4XpVKJPXv2oG27dkql0ob/UKs/3/vbb/eHTpumTbl5s0j/odPpkHT2LIJHjVL+9c8/1vUA/HjNYDjXQ6lUndTrYfg/8ElKSrFHp0NbhUKppNS2rz58+P7QiRO1KX/8Uaa+WqfTISk5GcEDByr/+vtv2+336tVzPXr2VJ08edJq+83pG/cOD8/XN7YruxQAEEJYLBZrnJub2zi5XN5YIpFo2Wy23RqilEKtVrMNBoOBx+Pty87Onk8ptTnOhhDSxs3NbZpare7J4/Eon893+Ewao9EIuVzOEwqFz1Uq1UatVvsNpdTqKj2EEBbAGge4jQPkjQGJFrBHLxSAigNABZAdgGI+pfSZraMJId3EYo+PNRpFMI8nMLDZPKfOLDIa9UStVvCEQslVuTzzO0rpVlvHEkLc+HzBZ1wud4ROp60gFIp1hNgT71IolQouh8PN0Ot1WzQa9QJKqc3BqoSQwe7uHpMUCnkrkUis43A4/6onnV6vJwqFgieRSK5KpUXrWsDjfcblckdodboKYqFQV1gig/93KKVQaTQcACoC7FCoVEXamYeb28cKpTJYwOcbeFzuWzGDWKPVsjQ6HRHy+UezZLKFlFLrK9cBIIQ0cnFxma7T6fqx2WyOQCAw2Eo/nAOlFAqFgsvlcjN0Ot0Wtbpom/Pw8Jgkl8tbicVlZ3NarZalUqnYEonkTGZm5reU0oOFyFhdLBJNBzDYSKlYJBDoi9LD24zRaIRcqeQJBQKTv9LpCvVXLBZrnJtEMk6uVDaWCIUO8eO2yGdnhBTPztzdTXYmEOTaGaUUao2mRP0EV9c3/QQez75+gtFoIHK5nCcWi+/JZNk/GI3G721lXiGE8Llc7hSRSDRKpVTWFkskWjaLZVXHWp3O1G7F4jOZUmnR7VYsNrVbo1EsEokKbbd5/UdmZin8h5WyDUYjkSuVPLFQeC9boShaD8AUETBKBdQWA1o2If8qH1xc9JQSBcCTAFczgaJ1zed/xuVwRmj1epu6diQGo5HIFQqeWCS6ly2XF7/9qlS1xWJx7jOisL6x3UGGhRBimFaWsy/p8BuUADIsP8EXIQMXgCcAh6RltIACkFJKS7TkqwP1IgeQWZL0UYQQHky5dHlFHWsnepjulaq4JxDTk9AdplUH7bWmbJjuTbEbNCFECFNbKZulLx1Heev6v87bbGfFRQ1TGyn2a2lCCBsmeyiYe9E6/wab08Kkh2IvSWx6QQQPAKVbFvjtobz9VVE4ys7Ks59ghOkaSjTfgxDiCtNKvLbe+Diz3TrDfzhLD/923nZf7aj7ZvNZ49Agg4GBgYGBgYGBgYGB4b8aPTIwMDAwMDAwMDAwlBNMkMHAwMDAwMDAwMDA4FCYIIOBgYGBgYGBgYGBwaEwQQYDAwMDAwMDAwMDg0NxSIYPQogLgD5ubm4RHA6nsjlLSblCKdXrdLoX2dnZPwE4VNTsfkJIDULIAHd3jzAWi7j/OxLwUI1SqbqtUil3AThXWJYOc8aCZnw+f5BYLG5JCBEVJ02l0WjMysrKSjYajYmU0qeFHU8IEQDo7uLi0p/H5dYkhNjVviilGpVKdVupUhX/+rjcQWKRqCUBiry+Esqi0+h0j+RK5W4AvxWVtYcQ0oDD4QxydXFpTwiR2JKFUqpUKBTX1RrNTgBXC8vUY84e0lYoFA4SCYWNCYtVaGaUXP0pndM+igulVKfRaB7J5fKS6c7VtVDd2airVHbv4e4eRlgsd3uu2S574fHsthdHUJ76syILKKVymUx2QafT7aSUFrqAhVl/oWKxOJrPF9QzZyJyGjm6kslK7mMIIQ7VVRFyKhUKxXWNRl2iZ4xQKGpsbqM55ei02uLbMQMDA4Mj1skIEggESe3ataPR0QNdqlatCja73GMM6PV6PH/+HNu2bc3+448/9CqVKthW/myBQPAhi8WeHxERaezWrYfIzc0N/4b86Gq1Grdv3zRs3rxJmZGR/lAmk3WymkKMEI6Li8segUAQNnzYMF6bNm24QqGwyGuklCIrKwtHjh5V/fTTT8RoNM5SqVRfWzuWEOIjFArP+jZuzB06cKBL7Zo1CYdjX59JrVbj5p07hk1btyrTMzIeyuRy29cnFu8R8Hhhw3v35rVp2pQrFAgcGiZq9Xo8fP6cbv7lF3nqy5dShUrVjlL6lxVZiFgs/o7NYo0ZHBVFOgUFCSQSiVVdm9eCwaUrV3QJO3ZoVSrV6WyZLMLaommEEImLi8sJDw+PRrEjRgj9/PzYAkHh2RfVajVu3rpl2LRpkzI93fHto7hotVo8fPSIbt68WZ6amipVKBSF647NHjN48GDSKTjYpu5skWP3W7dtK5bds9ns+ZEREcYePey3+1LZi68vd+jQoS61a9Wy214cQXnqzxLzWhw4ffq0Ztv27UaDwZAgl8vHWeskE0KqisWSczVq1KwYEzNCXL9+AxaX69xswnq9Hi9ePMeOHVuzb9wono8JD480du3aQ+TqWjY+hlIKjUaNK1cu6bZvT9CqVKrTMll2oc8Yd3ePRsOGxQqbNvVj8/lvnjE6nRaPHj2kW7dulj99atuOGRgYGHIxvy0q1Q+At0gkkiUlHaZGI31rfwkJCUahUJgJoKLlNRBCBlWpUlVx/34q1Wrpv/KnVhvoqFFj1K6urhes3SdXV9eNoaGhCpVSWWolPnv6lHp5eSnYbPYIK+3AXSgUpm9YtcpAs7Opo38GqZSOiY1Vu7q4WL8+sXhjaECAQnX+PKVXrjj9N++DD3RiofApAK6lLCKhcHpjHx95xpMnJbpGzevXtFvnzgpXV9dtNu7hyRHDh6sMen2J751Br6djxji3fRT3N2/uXJ1YLLauO5FoeuPGjeUZ6ekOqasou69ataoi9fFjp1xnsexl/XpDuT8c31L9Wf6kmZnUz89PLhaL51jRJ1ssljycPv0LrVJJaXn81q8v2sfcvJlKZTJabr/0dA3t3Llboc+YoUNHqORyQ5HXO2vWPJt2zPyYH/Njfjk/u75k8Pn8Oe+8884ny5eveFsWoLJJZGSEYv/+/R9RSn/Mu93T0/PK2rUbW/TtG1FeojkEg8GAqlUrqLKyslrQPMMKCCFiPp+f/tfz5/wKFSrYVcehQ4cwNCbmZmZmpm/e7YSQuO5hYSuSfv7ZaYtXGQwGVKhTx/r1cbnpfx0+zK/g7u6s6gvgGxWVffPRo2hK6ZE8shCJWPzPmSNHKjb38ytxmVKpFFXr19dotNrKNM8XB0JIXRcXl1sZ6emC0r7tNhgMqFCxolPbR3Hx9fXNvnnrVkHdSST/nDl9umLz5s0dVldEZKRNu9+4YUOLiAjn2X2h9tK9+y0FEr4AAB0iSURBVIqkQ4fe+sXeylN/lty+fRutAwIylUplRUrfDP0jhIR4ezfY/+ef91zKTBgrREdHKH79taCuPDw8r6xevbFFeHj5+xipVApv76oarVZj9Rnz118ZxX7GtGrlm3379s18dszAwMCQF7smfotEoiEDBw566wMMABg6NEbs6ekZk3cbIcRDqVQ27date3mJ5TDYbDaiogayAPSx2BXWokULjSM6kGFhYdBoNPUJIZXybvdwd48ZNmiQUztMbDYbAyMjrV9fo0aasgwwAGB4eLhEIhJFW2z2k0gkgma+vlbPKQp3d3e0DQjQAOhmsatPZGQk7BlOw2azMTA62qnto7gMHz5cIpFIrOuuWTOH1hUzdKhNu+/e3bl2b9NePDxihsXEvPUBBlC++rOkcePGqFixIgdAy7zbRSJR1NChw8VlKowVBg+OEXt4FNSVSqVs2qXL2+Fj3N3d0aZNW6vPmD59SvaMGTrUqh0zMDAw5GJXkKHRaCp7e3s7Shan4u3tDUppDYvNVStUqKApamz7v4WGDX34QqGwtsXm6o18fBwy2JvH46FK5coaANXy7SCkpnfduo6oolB8Gja0fn116pT5YPb6NWuy+Fyu5UVXr1enjsGesdaNfHx4ALzybuNyubUaN2pkdyP18XFu+ygu9evXZ/H5/IK6q1fPLt1Zozzt3qa9ADWZ52ap5THCwj74fEE9b+/65Z4psW5d67ry9Hy7fEzDho2sPmN8fBqXSEhv7/osHq+AHTMwMDDkYteDmVLK5vEc+yEjJSUFEydOLPI4F5eSvQjk8XiglFp2pjhcLs/qeLHU1FT4+5fujXR5wePxwOFwLB0Fhy8QlOg+L1++HI2bNEHMsGEF9nF5PAqAm28jpRxr7UAqlWLV2rUAgOTTpxEebd9LLx6XCw6bXfD6eLwy72DwOBxYyV7D4fP5Vo9v36ULACD1yRNs27XLZrkCPp8NC/1yuVyeLTurU4LgriTtQyqVYtWqVYWWl5qaCl8bw8JCQkORkpJiU46S6K6weorClt3zuFyrdp/3upOTkxHex/LDT8mwai+AVXspC+Lj4zF+/PhiH19S/RWFxMW+EU18Ho/AQp8sFuGWlz7zUlIfI5VKsXatqa2dPp2MqKhwZ4sIABAIBAWeMRyO7WeMLbhcq3bMwMDAkEu5v/2xpHXr1li+fHl5i/F/zarVq3H0yBFs3bLFrnKkWVlYtW6dg6T6d3Pu2DEAQOrTp9iWmFjO0hSNVCrFqtWry1uMMuf/9boZrGMwGJxWdlbWmyDDXvR6vUPKYWBgYHAkTgkyUlNT0bhxI4wcGQcfn4YYNiwGx44dQ4cOQWjYsAEuXryIixcvon37dmjZsgWCgtrj7l3TXNTk5GT06WN6ozNr1iyMGjUKoaEh8PauZzX4iI0dgb179+b+PWxYDPbt2+eQ69Dr9RgxIgZ+fo0xaFAUlEolrly5jLCwTggMbIXevbvj5cuXAIAHDx6gR48uaNWqOdq0aYmHDx9CLpeje/cwtGnTEi1a+GH//n25+sn7lWTJksWYPXsWAGDlyuVo1qwJWrZshpiYwQAAhUKBsWNHoX37NggIaJFbjiNYsmQJfP384Ovnh2XLlmHcuHF49OgRevbqhaVLl9pV9rQvv8TDx4/hHxSEj2fOhFyhQNTw4WjUqhViRo9GTtKBy1evolPPnmgVHIzukZF4+fffdl/XN5s2Yfn27QD+1965h0VVrQ38NyA3FcXPC2n6BKdHuSgDAwoCDqCYpZ1P0fCcFBTQ6uTxbqmdKP3oMS9paZZmHS+YaFrqKU92LMwLt0HlriKKJmqlhR5gbgy32d8fI8NtUHQ24nnO/P5iL/as9b7vXu/ae+39rnfBwnXrGPXKKwAcO32aqPh4Zq1cydCoKAZHRrL87kPlsdOniVi0yFhHcmYmE197zWxZALr2NUTMvLF8OakKBT7Bwaz/+GOz6+3d2xDuf/PmTUJCQ/GRyRji5UVqaqpZ9b7xt79x5coVfGQyFi9ezOLFixni5YWXVMq+fftanF9ZWcmLU6bg4enJxEmTqKy855YBD0xtbS1R0dF4eHoSOXky3333HRETJxr/n5yczMRJk8xup4neS5agVquJnDwZdw8PoqKjG/psdjahYWH4DR3Ks889ZxwHzKWkpMTYVr2uWq221fby8vIYHhiI1NubiZMmUVZWBhi+JM2fP9/YH06fPt2irdLSUl6IjGSYvz/D/P1JT083W/61a9cax+mFCxcyKjwcgGPHjhm/jMbHx+Pt48PwwEB+++03o96jwsORensTPno016/fc4uRNrF+/Vo2bzbIsmTJQsaOHQXAiRPHiIuL4ujRHwgLCyQw0JeoqMmo1WoA3N1deOutpQQG+nLw4Ff89NMVxo9/jqAgP0aPlnPxYpHZsgEsX/4GV69eISjIh7feWoxGoyY6OhJfX3dmzowy9rXc3Gyeey4UudyPiIhnuXXLcO3Hjg1j6dIFhIQMZfPmDyktLSUq6gVCQ4cRGjoMhcL862nBggUL5tBuXzIuX77MokWvceFCEUVFRezZs4fU1DTWrl3HqlUrcXd3JyUllZycXBIS3iE+/k2T9Vy8WMSRI99z6tRp3nkngZqapvv/zJgxk507EwGoqKggIyOD559/XhQdLl26yKuv/pWzZy/QrVs3PvlkEwsWzGXv3v2cOpVNTMwMli2LByAmJopXX51NdnY+KSkZ9O3bF3t7e7766h+cPp1DcvJxli59jftl81q7djVnzuSSk1PApk1bAFi16l3CwkaRkXGa5OTjvPHGYjQajdn6ZWdnsyMxkVOZmWQqFPx961b+8pe/0K9fP44fO8bChQvNqn91QgJPu7qSl57O2hUryC0oYMPq1RSeOcNPJSWkZ2ZSU1PD3MWL2b9rF9kpKcyYNo34d94xWze5TEZqbi4AWYWFqCsrqampITU3lxCZjHdnzyZr924K9u3jZE4OBZcuMXLYMIpKSii9+6C249AhZowfb7YsjVmdkIA8MJC89HQWPkDYSmucufvwuGfPHp4dM4a83Fzy8/Lw8fExT85Vq3j66afJy81l+PDh5OXnk5+Xx9HkZBYvWdLiofqTTz6hs4MDFwoLSfi//yM7O9us9ptz8eJF/jprFhcKC+nm6Mj58+cpKiqitLQUgB2JicyIizO7ncZ6r33vPXJzc9mwfj2F58/z008/kZ6ebuiz8+ax/6uvyM7KYkZcHPHx8Wa3XU9zXTdt2tRqe9NjYlizejUF+fl4DRlCQkKCsR6tVktebi6bN21ixsyZLdqZv2ABCxcs4Mzp0xzYv5+XXn7ZbNnlcjmpaWkAZGVno1arDX6XmkqIXI5Go2H48OHk5+URIpfz97vhlHPnzSNm+nQK8vOJmjqVefPnmy1LcLCc9HTDZDsnJ8soS3p6KkOGSFmzZgWHDx9FocjB13coGzd+YPzt//xPTxSKHCZPfpE5c17hgw8+IiMjm5Ur17FgwV/Nlg0gIWE1rq5Pk5GRx4oVaykoyGXNmg1kZRVy9epPKBSGvvb663PZtWs/qanZTJs2g4SEhr5WXV1NSkoW8+a9xtKl85k9eyEnT54hKekAc+a8JIqcFixYsPCwtNuCT1dXV7zuxlEPHjyY8PBwJBIJXl5elJSUUFFRQWxsDMXFxUgkkhaTh3rGjXseOzs77Ozs6NOnD7/99hv9+zesrQsNDWX27L9SWlrKgQMHmDTpBbOy8DRmwIABBAUFAzB1ajRr1qzk/PlzjB37DGD4lN63b19UKhW//voLERGGN6v1i/xqamp4++03SU1NwcrKil9++cX45q41vLykTJ8exfjxEUyYEAHA0aM/8O23h1i/fh0AVVU6rl+/joeHh1n6paWlMTEigi5dDIlZJk2caPYb8Hvh7+dH/yefBMBHKqXk2jWcunfn3IULPHM3FWZdXR19nZ3NbsvPw4PsCxdQqtXY2dri6+FBVmEhqbm5bFyyhC+Tk/ns4EFqa2u5efs2hVevIh00iGnjxpF0+DBxEyagKCjgcxEmPI+CYcOGMWPmTGpqaoiIiDB7ktGYtLQ0prz4ItbW1jg7OxMaGsqZM2donAkqJTWVeXPnAiCVShE7S9SAAQMIDjb4YnR0NBs/+ohp0dEkJSURFxeHQqHg8507RW0TwN/f3zje+Hh7U1JSgpOTE+fOneOZMYYEPfXjgFg013XlqlUm26uoqKC8vJzQ0FAAYmJimPynPxnrmTJlCgAhISEolUrKy8ubtHP06FEKCwuNx0qlErVaTdeuD5/4ys/Pj+zsbJRKJXZ2dvjKZGRlZZGalsbGDz/E1taWP/7xj8Zzk5OTAVAoFBw8cACAadOmsWTp0oeWoR6ZzI/c3AZZfHx8ycnJIiMjleefH09RUSGjRhnsXFNTjb9/oPG3kZF/BkCtVpOZmUFUVMN6surqKrNlM4Wfnz9PPmnoa1KpD9evG/rahQvnmDCh4Z7zxBMNfe2FF/5s/Pv48aMUFTVcT5XK/OtpwYIFC+bQbpOMxos4raysjMdWVlbU1taybNnbhIWN5ODBf1BSUsLIkWH3rcfa2tpk7Om0adNJSkpi3769bN++QzQdmme66drVEU/PwaSmKpqUq1Qqk7//4ovdlJaWcupUNjY2Ngwc6IJOp6NTp07o9cY07+h0OuPf33xzmNTUFA4f/ierV79Lbu5ZBEFg374DuLm5iaZbR2DXaGGhtZUVtXV1CILAYHd3FD/+KGpbNjY2uPbrR+I//0mQtzfSgQM5npXF5Rs3cLCzY93nn3MmKYke3boRu3w5uirDg0PchAn87/z52NvZMXn0aNEmrO1NSEgIKSdPcvjwYWLj4li0cCHTp0/vaLFEo7kvSiQS4uLi+N/x47G3t2dyZGS7XCtT448gCAwePBhFRobo7UFLXR0dHU22V1FR8UD1ND/W6/VkKhSImfnIxsYGV1dXEhMTCQoMRCqVcvz4cS5fvoyHhwc2NjZGOaytraltxzUPNjY2uLi4kpSUSEBAEF5eUk6ePM6VK5d56ilXRo16hp07vzD5286dDS9e9Ho93bs7cepUXrvJWY+trem+5u4+mGPHFCZ/Uy9nvazHjmWKej0tWLBgwRw6bOF3RUUFT959q52YmGhWXbGxsXz44QYAPD09zRXNyPXr18nMNAzue/fuISBgOLdvlxrLampqOH/+PI6Ojjz5ZH+++cawNqSqqgqtVktFRQV9+vTBxsaGEyeOc+3aNQCcnZ0pLf2dO3fuUFVVxXfffQsYbhI3btwgLGwkK1euQamsQK1W88wzz7J580eNYnRzRdFPLpfz9TffoNVq0Wg0/OPrr5HL5aLUDeDYtSuqu3HOreE2cCClt2+jOHUKuGvTCxdEaV8uk7Fu1y5CfH2Ry2RsOXAAmbs7So2GLg4OdO/ald/u3OFfjWLR+/XuTb/evVmxdStxIodKQdts8jBcu3YNZ2dnXn75ZV6aOZOcnByz6nN0dDROnuVyOfu+/JK6ujpKS0tJSUnB39+/yfkhcjl79uwB4Ny5cxQUFJjVfnOuX7+OQmHwuz179jAiOJh+/frRr18/Vrz7LnEihEpBU71bw83NjdLSUqM89eOAWDTXdXhAgMn2unfvTo8ePYxfH3ft2kVoSIixnvq1M2lpaXTv3p3u3bs3aWfMmDF89NFHxuO8PHEepOUjRrDu/fcJCQlBLpez5dNPkclkLSY5jQkKCmLv3r0A7N69W7RxKChIzoYN6xgxIoSgIDlbt27B21uGv/9wFIp0rly5DBjWvRUXX2rx+27duuHi4srBg4ZkDYIgUFCQL4psXbs6olbfu68NHOjGnTulnDrVcO0vXDDd18LDx7BlS8P1LCho/4mRBQsWLNyLDptkLF68hDff/Bu+vjKzM2M4Ozvj4eFBbKw4Dxr1DBrkxiefbMLLy4OysjJmz57LF1/s5803l+Ln583QoT5kZhreLu7YsYtNmzbi6yslJCSIW7duMWVKFNnZWchkXiQlfY6bmztgeMMWH7+M4GB/xo59xlheV1dHbGw0MpkXw4bJmD17Hk5OTsTHv01NTQ2+vlK8vQeTkPC2KPr5+voSGxODf0AAAcOH89LMmchkMlHqBujZsyfBAQEMCQhg8VtvmTzH1taW/bt2sXT5cryDgvAJDibj7oTDXOQyGTdv3yZQKsW5Z0/sbW2Ry2R4DxqEzN0d90mTmPrmmwQ322E6atw4Bjg74/GHP4giR2OkQ4ZgbW2Nd1CQKAu/6zlx4gTePj7IfH3Z9+WXzDczpr1nz54EBwczxMsLhUKB1MsLbx8fRoWH896aNTzxxBNNzp81axZqjQYPT0+WLV+On5+fWe03x83NjU2bN+Ph6UlZeTmzZs0CIGrqVAYMGGB26GA9jfVevGSJyXNsbW3Z/9VXLH3jDbx9fPCRycgQ8atGc13nzp3bans7ExNZvGQJUm9v8vLzWbZsmbEee3t7ZL6+vDprFttMZHnb+OGHZGVnI/X2xnPwYLZs2SKK/HK5nJs3bxIYGIizszP29vbIR4y4528+2riRHYmJSL292ZWUxIcbNogiS3CwnFu3bhIQ0CBLcLCc3r1789lnicTETMHfX8rIkYGtLujesWM3iYnbCAjwxs9vMN9+K07ijZ49ezJ8eDD+/kN4663FJs+xtbVl1679LFu2lMBAb4KCGu45zXnvvY3k5mYxfLiUoUM92bZNnOsZETGOX3/9VZS6LFiw8N9Fu8SCuLi4cPbsOePxjh2JJv938WLDm6MVK1YAEBYWRlhYGGDILtWYxnWqVA1vg7VaLcXFxcYYZDFwcXHh3LmWNx0fHx+OHUtpUT5w4EB++OFYi/LmoVX1zJkzjzlzWu4HcuJEWosyBwcHNm/+tC1iPzCLFi1iUaOMSgAlV6+KVv+e7dtNln/8/vvGv32kUlKOHBGtzXrCAwKoaZRV51KjLGSJjRbINictN5eXRchU1Bj13YXSNjY2HPv2W1HrBkM8fkxMjKh17tm9u8nx2rVrmxy7uLhw7uxZwNBH935hOvTEXFxcXChq5etWWloaL78k7gLX5nrX83GjSaGPjw8pJ0+K2m49nTp1ImnXriZlrbXn4+NDpsL0GBMdHc2GZg/rsbGxxMbGAtCrVy/23f16ICbh4eHUVFcbjy/dzRwIoG70lSgyMpLIyEgAnnrqKY6JHDIJMHJkOEplw3q/goKGe05Y2CjS0s60+E1RUUmTYxcXVw4dEn98Ati+fY/J8vffb+hrUqkP33/f8p7zr3+daHLcq1cvdu5smfnNXL7++jvR67RgwcJ/B2Z/ybhftqT25ujRo3h6ejBnztwW4QCNaU3OjpZfTARBQDChkJg6dqQdH4V+flOnUlBcTPS4cfeWpZV2hUZrbR6G1vQTQ8dHYb8HkKNl+QPazm/oUArOniXaxKaRjdsyWf5ALT08/+njTkfbr0W7pvqN8HjY8z/lWuv1pgR68DHGcP7jpZsFCxYeL8z6kmFtba1Tq9Wde/XqJZY8D8zo0aMpKbl23/PUajVWVlbaZsVarVZr3T6SPXrUajVVVVXlzYq1yooK0VZXajQaK6BJ/lyJRKJVi5BS936oNRrT+mk0oumXvcf0m8UWsmi1CILQPKBaa+56iwqlspZm9tXpdBVqEdZxPIr+0VY5xLBddis7ijdvy6TfazSPxO9b9ZdWdG38dcgcThw/bnYd0PH2a45SpRJoZk+9Xq8Swz/MRaMxbavKysfrHqNUVogyxmg0avT6Fn5swYIFC0bM+pJha2t7VowNnB4FaWlp+pqamuY7Ul1Xq1WSGzdudIhMYvPjj8mqmpqa5qvC806mpJh6gf3A3Lp1i3//+9+dgJLG5VXV1ZmpCkW7P6gmHz+uqqmtbalfdrYo+j0IJ7KzdUqNpnlw9NnzRUX25mxEdyI1tQ5osrJUEIS8H5KTlQ9d6V2Sjx5t1/7RVk6cPKlTKpUtbXf+vFm2M0Vrfq9Sq9vd71v1l6qqzNS0tEc6sXtYOtJ+zamuriY/P98eaDILU6mUGSkpx8XtOA9BRkYrtlKpJD///PjcY1JTT5gcY3788YcHGmNSUk7oVKoWfmzBggULRsyaZJSVlW3fvHmTpq4d0xCKgU6n47PPPtWqVKomgc6CINTY2toe2r59q3kxLo8BJSUlKBQZtkDzgP8clUqlPSlC/Pi2bdv09vb2RwRB0DUu12g0SVsTEyvFfkBsTMm1a2ScOmVaP41Ge1Lkzd/uRZlSyb7vvxdq6+qaBEALgvC7g7392X0HDz5UvRmnTlF6504N0Hzl+79ycnJsL1++/JASG/pHRkb79o+2UFZWxr59+4Ta2tqWtnNwOGtqN/GHRafT8elnn7Xq91u3tq/f39Nftm5tV38Rg462X3P279+Pra3tRUEQfmlcXldX99XXXx+Q3L59+1GK0wSdTse2bZ9q1WrTttq58/G4x2RmZnDnTqnJMSYvL8e2PtvW/SgrK2P//pZ+bMGCBQuNMXdNxpfnzp07O2XKi7rH9WtAcXEx48aN1ZaWlh4HWsQQKJXK+PXr15WtW/eeXqk0+2XxI0ev15OenoZcHqiVSCRLm4ehCIIgqNXqGeMnTKg8dOhQq5se3guVSsUHH3ygX7lqVXlFRYWpXbLSysrLvx8TEaG9WFz8sKqYRK/Xk6ZQEDh6dOv6VVbOGL9gQeWhkycfSr+2IggC+ZcuERwXp6nT67cKgtDijlxWXj5r9muvqRN376atD5G1tbUcPnKEsZMmVep0upcEQWjyQCIIglav1y8MCg7WpqSkNNlj5X7o9XrS0tIIDApqt/7RFgRBID8/n+ARIzR1dXWmbVdWNmv2nDnqxMTENtuuNYqLixk7btw9/X7d+++Xvfee+H7fJn8pK/t+zLPPai82WhT9ONGR9muOTqcjKSmJl195RVNeXv5q8/8LgnAN+HjUqGBNXl7uI18DcflyMRERY7W3b5u2lUqljN+4cV3Z+vUdd4+pra3lyJHDTJo09p5jzMiRQdq0tNbHmPoUvuHhwRq93rQfW7BgwUI9EnMHZIlE0rlbt26bq6urI/v371/3xBN9BRubjt/ArLq6mhs3frYqLf1d6NSp0w6VSvW6IAgmc+VKJJKnnZyc/l5ZWRns5uZe6eTkhJVVh2X3bTN3s2rZ1NXV3tbpdAlVVVWmUzkBEonkWScnp/W1tbWuHu7uVV26dEFyHx0FvZ6y8nKKioocHBwcMsvLy18RBMHkU5FEIrHu2qXLar1e/1Kvnj2tB/Tvr7dttPneQ+t35YpNbW3tbV1V1f31c3Q06OfqWtXFweGeefkfFF11NVd//dVardFU6gVhg1anW9VajJFEIhnaw8lpc6VOJ/V0c9N1c3Q0aWtBr0ej1XLh0iW7TtbW15Qq1et1dXWtpp6ys7Ob7uDgkGBlZdVn0KBBNZ07d76nzPX9o7a2ffpHW9HpdFy9etVarVZX6vX6DVqt9t6269Fjc2VlpdTTw0PXrVu3B5Kjurqan2/csPq9tPSB/N7dza3SqUcPs/z+gf2la1eDv/TqZT1gwACz/UUMOtJ+zRH0elRqNYWFhfb29vbnysrK5giCkNmKLBIHB4fXra2tX+vcuUtXV9c/1Lb3pnTV1dX8/PPPVrdv/y5YW3faoVbf21bduxtsNWjQo7vHCIKARqPh0qULdtbWna6pVMr7jjH29oYxZuDApmOMTqejpKTBjysrW/djCxYsWAARJhnGiiQSG8AP6EkH7r/RiDrgdyBXEIQ2xXNJJJLugAxwbE/BRKQKKBEEoeUuUq0gkUgGAG6AQxt/ogLyBUEoa2P9Vhhs2AfzUyQ/Cv3aSg3wK3C2rTdWiUTSBxgCdLnHaZVA8d23sW1CIpEMBFwBu/uc+rjYr71sZ4qO9vuO9Bcx6Gj7NUcDFAqCcKuNskiAwUB/wKYd5GnM42ar1hBrjHlgP7ZgwcJ/N6JNMixYsGDBggULFixYsGAB4P8B9MveVUegEtMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92c0d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92b72860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92b4d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=175): without heavy-handedness , dong provides perspective with his intelligent grasp of human foibles and contradictions .\n",
      "Pred class : positive ✓\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92b3d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92a2df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92a38080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92acff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=1793): a frantic search for laughs , with a hit-to-miss ratio that does n't exactly favour the audience .\n",
      "Pred class : negative ✓\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffae5644a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92c5c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92a8c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92bbcdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=1186): what you would end up with if you took orwell , bradbury , kafka , george lucas and the wachowski brothers and threw them into a blender .\n",
      "Pred class : negative ✗ (positive)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92a4f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92a55518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92adeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa929d8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=354): a charming yet poignant tale of the irrevocable ties that bind .\n",
      "Pred class : positive ✓\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa92927898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa900c3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vd8uub7p06eKpVCo7WwW3D2zfXu/hUX8TU7sEBXkq5fKqenTo4BI9GGPo3LGjEVb1qlQqQ7p2DXP7cMLq7NyxY2C92vluJDS0i6dCcWdtExwcAsaY9fOmpb9/A4OfX/22923btofRaFIAqDRpRafTtQkJuTNP/uDgEJSV6VpahjHGZDqdNqB1a/s/tSoUPvD3b1qlTePc3zh1F5vNZqlCoXD4fLlcThD2l6uAAC97c5wKYI3D0u3nGCw2wHMBCgBSYQ8/S7y8lUqn6qOouBipaWk1pvFWKj1hZXOZTKZ0ph4tmT59Ov744w+X5GUPaWlpyMvLsxmnUCggbgRtiZfSRlmLiouR+t13tcrzadu2zjoq5HK79aiJz7/+Gp2jopD0xBNV4ryVSgaremWMyW3Va1FRERYsSAUA7N27F0OHPlYnPayRyxUAbJRPaXtyZ9++9dML6ChLlqTh+eeftRnXoIH1bVsz1V6DNmxjWS95eXkYO3Z0nWTVpAMRWQ+I8/LyktucKVdcXIS0NEGPX37Zi8mT63Z9rFqVhr/+sn1PAoBMJjPC6lqtri3JyspCWFhIlfAnn6z7c6a6ulMoFDCZjNYvG16enlJTXcdmyWRV2zTO/c3/7GsiAahxrq0F3QF87kZdXEVRSQlSFy++ozosXLgQQUFB9SYvbfHiap2oumCPA3qnSf3uO+xcuxbLv/nGqXyKiorw1VepLtKq7uzb90uVMKOx8uxwIoLZbO8den9gWS8tWrTA6tX18YpYlZKSIixe7Pj1sXp1Gm7ccP6erImvv67f5wyHUx1uc6KGjx2LiN69ERwRgW8WLXJJnksAdAHQFUCyGLYPQG8AD+HvXikVhEE/4QBCAawXw7MABAKYDGEhkmwI3UEvAwgGkADgCIA4Mb8N4nl7AZS/i80CkGKRxtK5miPm3wfABAAfOVNYkbc//BCffvttxf9vvv8+Plu4EPNTUxE5cCC69OuHd+bPBwC8Nm8eLl+7hrCEBLz87rsukC68CXbq3BlJkyahc1AQRo8ZA41Gg927d6NbeDhCu3RBSkoKdDphnHpcfDyOHTsGAFi0aBE6BgaiR1QUZsyYgWefFd7upz7+OGbOnIne0dF4qH17rFkj1JxKpUK/hASER0QgtEsXrF+/vkKHzkFBmDFjBoJDQpA4YAC0Wi3WrFmDY8eOIWnSJIR16watVutwOV+bOxeXs7IQFh+Pf771FvqNGoXwfv0QGhuL9Vu32jxn/hdfIDIxEV1iY/HOBx84LNsWn3z5JUJ690ZI7974dMECPPXii7iSlYWBY8fiP6nOOUBvvPEaLl++jIiIMLz22stQqVQYO3Y0goM7ITk5CeVj048fP474+Fj06BGBgQMH4Pr167XkbB/lPQIZGXsRHx+DESOGokuXIGRlZSE4OBCPPz4ZYWEhyM7Oxs6dOxAT0ws9eoRj/PgxUKlU2L59G8aPH1ORX0bGXgwfLtyhK1euQLduoQgLC8Hrr79akWb79m3o0SMcERFdMWCAMCTw6NEjiInphcjIbujbtzcuXLhQkT47OxsJCXEICnoYc+bMtlmOjz+ej169IhEe3gWzZ7/jtF0s62XcuDHo2lXogTGZTHjllZfRs2ckunXrgm+++RoAcP36dcTF9UVERBi6dg3B/v37ndYBAObNew3Xrl1GQkIY5sx5GWq1CjNmjEZMTCc888zf18cnn7yLgQMjER8fgpdffgJEhE2b1uC3347hmWeSkJAQ5tQ9WY7RaMTkyUkIDe2MceNGQ6PRICEhDsePC8+ZBg188NZbbyIioiv69OmJGzduAACuXr2KmJhe6NYtFG+//X9O68Hh2ISIHD7kcrm6IDubSKOpctzKySHSaEhz6xYFBwWRrXQPd+hQBCDCMs8GwKVjAJHV8TtADwN0U/z/FkBTABoNkAmgswC1F+MMABWLv2+K4WaArgLEADpkkS8A2iL+Hg5Qf4D0AJ0CqKsYng7QYPH3OwD1AqhMzLuhmP6ImF4LUAlAHQCab6McBNDnAPkC31qWG8BzTyYnaykvjyyPq7/+St1CQojy8siUk0MPtWlDKxcsoBlJSWTOzSVTTg4NTkigjLVr6eqvv1JwYCBZ52F5PD99uh7AS5aylUrl5x/Nn09kNlc5rl65QgDowP79RGYzPT51Ks15911q1aoVXTh/nshspuTkZPrPJ58Qmc0UGxtLR48codycHGrTpg3dKiggvU5Hffr0oWeefprIbKYpU6bQ6NGjyWQ00tnff6f27dsTmc1k0OupuKiIyGymm/n51L59ezKbTHT1yhWSSCR08sQJIrOZxowZQ0uXLKkkz5buG9avp8aNG++zsvOQ+OjoIsrPJ8vj6rFjFNypE1F+Phny8qj48mWi/Hy6ee4ctW/blsw3bhDl55O3UkmUn0/bV62iGcnJZL5xg0x//UWD+/enjPXryTpfys+nDUuXUuOGDavq0adPEd2+TdbHsT17KKRzZ1JlZ1Ppn39SUGAgndi7l9q0bk03MzOrpKfbt+nRfv2KAYyylNGoUaOdq1evIaORKh2XLl2l4OBgMhqJdu1KJz8/P8rKyia93kRRUT1p7979pNXqqWfPXnT9ej4ZjUQ//LCSpk59vEpeRiPRunUbqFGjqnaOi4sv0uuJrA9vb2/S64l27kwnpVJJFy5cIb2e6OLFq8QYo/37D5FeT5SXd5P69ImhwkIV6fVE8+a9T2+/PZs0GgO1bt26IvyJJ56i779fSllZudS6dWvKzc0njcZAcXHx9OOP6yg3N1+4XkU5f/11i/R6ooKCYtJoDKTXE23dupOGDx9Jej3RwoXfU/Pmzen69QIqLtZQUFAwHTp0tJLumzdvp2nTZpBOZ6ayMhMNGjSYdu/OqFLWtWurt01N9WL5e8GCr2n27DlkNBKp1WUUERFBmZlX6MMPP6J3351LRiORTmekwsISm/Vz8uRpCggIuGalQ2irVm2LbT0mfv31KgUGBlNeHtGaNenk6+tHx45lU06OiSIietK6dfspL4/o7NlbFeeMGjWJ0tI2UF4eUa9esbR169FqH0NKpbcWQCPrtuT69YIq9rt48SoBoL17D5BeTzRlyuP0/vvzqW/f2Io6AUBr124gvZ7oxRdfplmz5pBeTzR48BBatGgx6fVEn332RUXdWR+3bpWQVCots7KPr1Tqpd+yhaguR4sWD1dp0/hxfx9uG8j8eWoq1m0U1h3LzslB5uXLaNSokcP57QEwBkBj8f+G4t/hELrTggDcEMMIwBsQeqk8AORaxLUB0NMiXxmAR8XfoQC8AEjF31nV6DJYTOcFoKmY90EAwyB8DJdDWPLbFbRt3RqNGjTAyTNncKOgAN1CQnD01CnsyMhAt/79AQAqjQaZV67gwZYta8nNMVq3bo1ocRbZpEmTMGfuXLRr1w4dxbWNpkyejC9TU/HCCy9UnHPkyBHExsaiYUOhpsaMHo2LFy9WxA8fNgweHh4ICgqqeHMkIrzxxhvYt38/PDw8kJubWxHXrl07hIWFAQAiwsORlZXllrJW6DFvHvYdOiTo8ddfuJGfj+YWa3ft2LsXO/buRbdHHgEAqNRqZF65gr69ejkt/8DhwxgxeDC8vYWx/iMfewz7Dx1yOt/qiIzsgVathLGwYWFhuHYtCwEBATh79nc8+qhwjZlMJjRv/oBbZLdr9/d4+DZt2iAqSrhDf/31MM6d+wOxscK1p9fr0bNnL3h6eiIx8VFs2rQRo0aNxtatm/Heex8iPX0P+vaNQ5MmTQAAEyYk4cCBfZBIJOjTp2+FnPJrsri4GCkpU3DpUiYYYzAYDBV69OvXv+J5NXz4SBw8eAAREd0r4nft2oFdu3YgMrIbAECtVuHSpUzExPR1uY127tyBM2dOY+3aNRV6Z2Zmonv3SMyYkQKDwYBhw4ZX3B+uJiysB1q0EK6P4OAw5ORkISqqD375JR2pqR9Cq9WgqOg2OnYMRmKiq558f9O6dWv07i1cAxMnTsIXX1QeXCGTyTB4sNATGR4egd27dwIADh06iNWrfwIAJCUl4403XgWH42rc4kTt3bcPu9LTcSg9HUqlEnEDBqCsrNadKRzCcrRk+QI5ywHcBHAcgkPUFkC5dOspaFIA5UMHPSzy8wBQ3Rr+ljIlNaRzFdMnTkTa6tX4Kz8fKePHY/eBA3j9uefwZHJypXRZ2dlukW89uDIgIAC3bt1yKk/LhR/FNz8sX74cNwsKcPzYMUilUrRt167iurFML5FIXPKZoDqW//QTbt66heO7dgl6RESgTKerlIaI8PrMmXhyiq2Z0fcW1rY1Go0gIgQFBePgQfc5bwAqHMVylMq//yci9OvXH8uWrahy3tix45Ga+gUaNmyI8PDucGR5jlmz3kJcXDzWrFmHrKws9O8fVxFnfc1b/09EeOWV1zFjxpN1lltXiAiffvpfDBgwoEpcevo+bNmyGdOmTcULL7yI5OTJVdI4i+X14eEhXB9lZWV4/fWnsXXrMbRs2RoffTQLOp17nvG11YVUKq0IK79+q0vL4bgat4yJKi4uRoOAACiVSpy/cAGHjxxxOs9HAPwIoLzpvl2TfAg9RFIA6QCuOS29dqIBbITgrKkAbHJh3iMGDsS29HQc/e03DIiLw4DYWHy3ciVUajUAIPf6deQXFMDX2xulKpULJQv8+eefOCT2hPzwww/oHhGBrKwsXLok7JSwdNkyxPat/AYeGRmJjIwMFBYWwmg04qe1a2uVU1xcjKZNmkAqlSI9PR3XrtVec76+vigtrXVLwNrz8fGpsF1xSQmaNm4s6HHgAK7ZcE4HxMfjuxUroBLPyb1+Hfk3be6sUWdievXCz1u2QKPRQK1WY93mzYhxQQ9XOfbYLDAwEAUFNyvq3WAw4OzZsy7TwR6ionri0KGDFdeZWq2u6M3s2zcWp06dwKJF32LsWGEZpsjIHti/PwMFBQUwmUxYtWoFYmJiERXVEwcO7MPVq1cBALdvC0+P4uJitGgh9N4uWZJWSfbu3Ttx+/ZtaLVabNjwc0VPSDn9+w9AWtp3f9d/bi7y8/OdKm919ZKYOABff72goqfs4sWLUKvVuHbtGpo1a4bp02cgJWU6Tp484ZT8cry9faFS1Xx9lDtMDRs2hlqtwubNfw+Ct+f8uvDnn3/i8GHhOly58gdER9u3lmWvXtFYtWolAGDFiuUu04fDscQtPVGPJibiq4UL0blbNwQ+/DB69ujhdJ7BAN4EEAuh96dbDWmTIHxOC4Uws64+NpyIBDAUwsD3ZqJsV63AIpPJEB8djQA/P0gkEiTGxeHcpUvoNUToOvfx9say//4X7du2RXRkJELi4zEwPh7z337bJfIDAwPxZWoqUqZNQ1BQED7/5z/Rs2dPjBk7FkajEZHdu+Opp56qdE7Lli3xxuuvo0dUFBo2bIhOnTrB379miyQlJWHI0KEI7dIF3bt3hz3bYUydMgVP/eMfUCgUOPTLL3B0qYZGDRsiukcPhPTti8iwMJzPzERobCy6d+0KW1vDJMbH41xmJnoNHgwA8FEqsSw1FU3FT0nOEN61K6ZOmIAeCQkAgOnJyejWpYvT+ZbTqFEj9O4dja5dQ6BQKNC0adUthmQyGVatWoMXXpiJkpJiGI1GzJz5AoIdXBfOEZo0aYKFC9OQnDyhYuLC7Nlz0bFjR0gkEgwa9BiWLEnDd98JM1IfeOABzJ37Pvr3jwcRYeDAwRg6dBgAIDX1G4wdOxJmsxlNmzbF1q078a9/vYKUlCl47725GDhwcCXZ3bv3wLhxo5Cbm4MJEyZV+pQHAP37J+L8+XOIiRGcWx8fH6SlLUPTpk0dLq9lvXTq9PfSUtOmTUdWVhYiI8NBRGjcuAnWrv0ZGRl78fHH8yGVSuHt7YO0tCUOy7akYcNGiIyMRnx8CORyBZo0qXp9+PsHIClpBh55JARNmjRH166RFXHjxk3Fq68+BblcgY0bDzl8T5bTsWMgFiz4EjNmpKBz5yA8+eQ/sHlz7VvUffLJZ5g8eSI++ugDDBkyzCkdrHn77UF4/vmFaNSohUvz5dx7OLXti0KhUOdkZiodHevUsUuX4sxLl/oR0fHysIaMXdoJtI9wWKs7hwrCbD8NhC3Rv4EwQ9Ca/wJ4E1gSp75UAAAI40lEQVRYQjSjPIwx9tyTyckffvXBB1XWGDGbzQhPTMSP33yDhx96yCkdX3j7bcNnCxe+TkQfl4d5e3t//u7s2c+99NJLVdJnZWXhsSFD8PuZM3WWpVKp4OPjA6PRiBEjRyLl8ccxYsQIp/SvCxs3bkTKtGn7b968WdFNxhgbEh8dvXTPunX1tsrgxu3bkfL88/tv3rpVWY8+fZbu2bDBJXoMHDOmZNvu3SlE9FN5WOPGjXcuWPB1wsiRo1wholo2btyI6dNT9hcUVLZzXFz80h079vxPr968adNGzJhh2za7dtWPbc6cOYP4+L5/FhYWVmyHwBgLbdWq7YEjR67W+0aZHTr4lGk06lZEVDEmQKFQqK9ezXG4LXGG0tJSNG/eSKfX6yuevYwxX6nU69b69WV1Wqx2+vSOxXl5mZXaNM79zR1dJ8oZB+5u5AkAYRAcp1Gw7UAB4pTAv4dw1cgfFy+iQ+/e6BcT47QDBQBUj0afNWsWwrp1Q0hoKNq1bYvhw613unEv5bMn7jREZF9lOynjziFe0ZwqCPVyZ21zN9wDlhDRXTVQybX2ubtszXE/Tn3O8/DwMDgzwLesrMwDf4/5BgAwYXPee5If7EynBWAQ9iK2RKfWaKqsLhjUsSOuHD7stG7lqDQaE6xsrtPp1NXVY9u2bR3qhQKAjz5yxUpZjqPVakFWO9cD0GncOCjdph5lZW7XQ63REKzq1WwmrTsH4JdTrZ011kH/e9wNttFqtWCM6ayCdTpd2R15iTYY9BJYXavOtiXOoNVqIZF4GqyCdUajwZOI6jQ4Xa+v2qZx7m+cuom8ZLIrJ3/7zaFzS0pKcLOgQAbgT8twE3D2xH3uzv8KqHXAOavgi0dOnXL3RD8cPXXKACDTMsxkMl345dAh149Iv8OcOHnSoNFoTlsFXzyXmSmzXiHbrXqcPm3QaLVV9bh40SV6mM1mnD1/XgqrelWrVb8dO3ZM77SAWjh58oRtO587V692vhs5dcq2bc6frz/bnD79G8xmsn7e5BQXF0oLC2uaouN6Ll26AKlUpoIw6qECLy+vK6dOnaxXXco5ffo3yOXySrNYiEjv5aW8lZX1u935aDQlKC6+WaVN49zfOLtX2/ffL13q0CvVTz//DKVCcYiIKk3jKAaWLgJU96sXVQJgq9AD+LNV1IHs3Fx2PjPTxlmu4XJWFjKvXJFAmLRoyYY9e/ZICwsL3Sa7vjEYDFiyZIm+rKxslWU4EWV5MJaze9+++tNj9erq9cjIcFpGxsGDMJlMN4noomW4Xq9f/cMPy416vfv8KIPBgKVLq7Gzh0fOnj273Sb7bqcm2zDmkbN7t/ttQ0RYuPDb0uLioiVW4Rq5XLF306Yf3a6DJWvXLjMzxlZbDysoKir6fsmS7+9I1+WSJd+XlZQUp1mHE5lXpKcvs9vTPXDgJ3h5Kau0aZz7G6ecKCJavmP37ttz33/faO9bFRFh1549eO7FFzWFRUVv2Uiy/Spw5QnATauO3DkKAMQBaimwhIgqzYcmIiOAtxPGjdNcdsNCklnZ2XhkzBgNY2wOEVXquiai2zKZ7Nu4+Hi1s9O07wZUKhVGjBypUavVhwFUmfddolK9PO6JJ7THHexFrZMeU6dq1BpN9XpMm6Y9fuqUwzJOnTmDkZMna9UazSs2on/X6/Xpw4cP1bhiGQhrVCoVRo0aUa2dS0tLXk5KGqc9ceJ/b4ytSqXCmDEjNBpN9baZOHGc9vhx99lGp9Nh5sxndRcunM8GsNk6vqSk6J3Zs19Sp6dvc/u4KSLCmjVL6euvP1ZpNOpPbMQv37lzx+1//3uu3W2JsxiNRsyf/4Fx48aNRWazucrURp1O89nGjV+odu78nmrax5GIcPLkLixY8JxGpSq01aZx7mOcmp0HAIyxFn5+fpvJbA58NDHR/GCrVl4ST88qH5GJCLdv39Zv37XLVFhYWKbWaCYQ0a5q8vTzB9bpgegEwNAe8JLdw5slawDTKaDsqFCORaXAc0Rk865UKBT/JKK5nTt0MPbu3t3LW6n0ZB6OFZ3MZqg1GuOh48d1f1y8KJVIJO+qNZr3baVljDFfX9+P9Xr9PyIiIvTh4eFeSqXSbSvauwNdWZn53PnzZRkZGTIvL6/NJSUlE4nIeiwIAMDDw2Os3Mvr25YPPMAeiYmR+vv6Sl21MJ9Orzefy8wsy/jlF5mXTLa5pLS0dj1atBD08POrVQ8iQnFJiWHvwYP67Jwc6A2GZ4xG4zJbaRljMj8//8U6XdmIPn1i9EFBwV5yuZfEmfKVlenMFy6cs9/Ocvm3LVu2ZHFxj0j9/f1dZue7EZ1OZz5//lzZvn11s018vOtso9frzVevXtXt3LlDKpPJfi0uLh5GREW20jLGYpVK79V+fv7KuLgBkgYNGsk8HHze2MJkMlNBwQ39nj1bSacru6VWq4YSkc23l4q2hChwwIBHza1aPejl6Slx+cViNJooNzdHt23bVg8Al0pLSx4jIpuf4BhjwQqF70ZPT1mzyMhBCAhoJvPw8GCAcB+Wlt7Wnzix3VRaWlim06mrbdM49y9OO1EVGTHWDsK+v41QvcNTCuAogCP2zBJjjDUHMADC2pn3VINuhQ7Cmp/biajWsUeMMRkEWwYCcG6RFWEceyaAXdU9zK1kewNIBNAOlRdnvxcwAMgDsI2Iah3swRiTQNgvuiuqLmZ/t+uhBvA7gH1iL2ZtMhpA2OGoJYR1aJ3hbrHz3cjdYBsTgHwAO4gozw4dGIQl9aIA1H3p95ohCOsf7wdw1s7nvj1tiTOYIazXvJuIrthzAmMsCMLKNQH4e5MLoI5tGuf+w2VOFIfD4XA4HM7/EvfsJzIOh8PhcDicOwl3ojgcDofD4XAcgDtRHA6Hw+FwOA7AnSgOh8PhcDgcB+BOFIfD4XA4HI4DcCeKw+FwOBwOxwG4E8XhcDgcDofjANyJ4nA4HA6Hw3EA7kRxOBwOh8PhOAB3ojgcDofD4XAcgDtRHA6Hw+FwOA7AnSgOh8PhcDgcB+BOFIfD4XA4HI4D/D/ElqJBNgEacwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa90081550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa9005ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review(id=1043): but he loses his focus when he concentrates on any single person .\n",
      "Pred class : negative ✓\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa7afca5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa7afd9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa7afe7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa7af5cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Traverse over the analysis results and visualize them.\n",
    "for i, idx in enumerate(test_sample_indices):\n",
    "\n",
    "    words = [decoder[t] for t in list(DATASETS['testing']['encoded_reviews'][idx])]\n",
    "    \n",
    "    print('Review(id=%d): %s' % (idx, ' '.join(words)))\n",
    "    y_true = DATASETS['testing']['y'][idx]\n",
    "    y_pred = test_sample_preds[i]\n",
    "\n",
    "    print(\"Pred class : %s %s\" %\n",
    "          (LABEL_IDX_TO_NAME[y_pred], '✓' if y_pred == y_true else '✗ (%s)' % LABEL_IDX_TO_NAME[y_true])\n",
    "         )\n",
    "                                \n",
    "    for j, method in enumerate(methods):\n",
    "        plot_text_heatmap(words, analysis[i, j].reshape(-1), title='Method: %s' % method, verbose=0)\n",
    "        plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {
    "height": "673px",
    "left": "0px",
    "right": "1228px",
    "top": "110px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
